id string | sources list | title string | abstract string | authors list | categories list | fields_of_study list | published_date timestamp[s] | url string | pdf_url string | arxiv_id string | doi string | citation_count int64 | influential_citation_count int64 | has_code bool | code_url string | venue string | quality_score float64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
6c9feedfc3fd2bcf5e98fc45593cfff5ac905d7e207ef8afa93e0893111f7223 | [
"arxiv"
] | Understanding Key Features of Time Series Foundation Models from Epidemic Forecasting | Seasonal influenza infects millions of people and causes substantial morbidity and mortality in the United States each year, making accurate short-term forecasting a core public-health need. Reliable forecasts of epidemic time series can inform vaccination timing, hospital staffing, and resource allocation, yet the com... | [
"Alireza Jafari",
"Judy Fox",
"Geoffrey C. Fox",
"Madhav Marathe",
"Aniruddha Adiga"
] | [
"cs.LG"
] | [] | 2026-06-17T00:00:00 | https://arxiv.org/abs/2606.19560 | https://arxiv.org/pdf/2606.19560v1 | 2606.19560 | null | 0 | 0 | false | null | null | 0.35 |
0f62e3b10a488c5cfe8f3ce1e36f42cdb897e7274bb0ff99fc9ef4ed29d99a65 | [
"arxiv"
] | ForecastBench-Sim: A Simulated-World Forecasting Benchmark | Forecasting benchmarks for general-purpose AI systems usually inherit the constraints of the real world: outcomes resolve slowly, tail events are rare, and counterfactual questions are difficult to score. We introduce ForecastBench-Sim, a simulated-world forecasting benchmark built on game rollouts from Freeciv, a turn... | [
"Jaeho Lee",
"Nick Merrill",
"Ezra Karger"
] | [
"cs.AI",
"cs.CL",
"cs.LG"
] | [] | 2026-06-17T00:00:00 | https://arxiv.org/abs/2606.18686 | https://arxiv.org/pdf/2606.18686v1 | 2606.18686 | null | 0 | 0 | false | null | null | 0.35 |
71eae1cfc0a5f8721ace544da8c3f024ed2d08def95c9cf1f8ed50947982ee43 | [
"arxiv"
] | Do Time Series Foundation Model Benchmarks Hide Regime-Dependent Failures? Evidence from Traffic Speed Forecasting | Standard benchmarks evaluate time series foundation models (TSFMs) using aggregate metrics, but these can mask severe failures in critical operating regimes. We introduce regime-stratified evaluation and apply it to three TSFMs on two standard traffic speed benchmarks. Traffic exhibits abrupt regime switching between f... | [
"Yingshuo Wang",
"Xian Sun",
"Lingdong Kong",
"Wei Gao",
"Yanhang Li",
"Zhichao Fan",
"Zexin Zhuang"
] | [
"cs.LG"
] | [] | 2026-06-16T00:00:00 | https://arxiv.org/abs/2606.18367 | https://arxiv.org/pdf/2606.18367v1 | 2606.18367 | null | 0 | 0 | false | null | null | 0.35 |
84efca45803599bea40694f1971e37ec7bba670ac632bcd7e36bd7ba32eccb4f | [
"arxiv",
"semantic_scholar"
] | TimeVista: Exploring and Exploiting Vision-Language Models as Judges for Time Series Forecasting | High-quality time series forecasting is pivotal for real-world decision-making. However, traditional point-wise metrics often fail to reveal complex temporal patterns and align poorly with human intuitive preferences. While the ''LLM-as-a-Judge'' paradigm has revolutionized text evaluation by providing flexible, human-... | [
"Zhi Chen",
"Yuxuan Wang",
"Jialong Wu",
"Yong Liu",
"Haoran Zhang",
"Xingjian Su",
"Jianmin Wang",
"Mingsheng Long"
] | [
"cs.AI"
] | [
"Computer Science"
] | 2026-06-15T00:00:00 | https://arxiv.org/abs/2606.16173 | https://arxiv.org/pdf/2606.16173v1 | 2606.16173 | null | 0 | 0 | false | null | null | 0.35 |
4a528ac52154dc185b0cf8fb0896ac6e5c664c4ffa6c42c1e035c35f7150dfc8 | [
"arxiv",
"semantic_scholar"
] | Quantum-classical hybrid models based on error correction for time series forecasting | Time series forecasting largely benefits from combining the strengths of different models, especially using a scheme where a model corrects another model by capturing supplementary patterns from forecasting errors. Concurrently, quantum models are providing a means to augment the classical capacity, including in time s... | [
"Jonathan H. A. de Carvalho",
"Filipe C. de L. Duarte",
"Fernando M. de Paula Neto",
"Paulo S. G. de Mattos Neto"
] | [
"quant-ph",
"cs.LG"
] | [
"Physics",
"Computer Science"
] | 2026-06-13T00:00:00 | https://arxiv.org/abs/2606.15213 | https://arxiv.org/pdf/2606.15213v1 | 2606.15213 | null | 0 | 0 | false | null | null | 0.35 |
8e31223be06f17e56eef817edb064faf2f7ae673f1dafd293526305187a25676 | [
"arxiv",
"semantic_scholar"
] | Semantics-Enhanced Retrieval-Augmented Time Series Forecasting | Time series forecasting models often benefit from historical patterns. Inspired by Retrieval-Augmented Generation (RAG), recent research explored retrieving relevant historical time series segments to enhance forecasting. However, relying solely on time series similarity is often insufficient for retrieval under non-st... | [
"Shiqiao Zhou",
"Zipeng Wu",
"Holger SchΓΆner",
"Edouard FouchΓ©",
"IAG Wilson",
"Shuo Wang"
] | [
"cs.AI"
] | [
"Computer Science"
] | 2026-06-12T00:00:00 | https://arxiv.org/abs/2606.14941 | https://arxiv.org/pdf/2606.14941v1 | 2606.14941 | null | 0 | 0 | false | null | null | 0.35 |
51523f42010f1fcec9caf40068f2a3a388bc2159ba442d9efdd26a2c6ba25ea2 | [
"arxiv",
"semantic_scholar"
] | APEX: A Network-Native Time-Series Foundation Model for Forecasting and Anomaly Detection for Wireless Edge Operations | Generic time-series foundation models transfer poorly to wireless network telemetry whose signals are bursty, zero-inflated, and coupled across protocol layers. We present APEX, a network-native, decoder-only transformer for forecasting enterprise AP telemetry, and evaluate it on DHCP degradation as a representative ne... | [
"Swadhin Pradhan",
"Niloo Bahadori",
"Peiman Amini"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2026-06-10T00:00:00 | https://arxiv.org/abs/2606.11553 | https://arxiv.org/pdf/2606.11553v1 | 2606.11553 | null | 0 | 0 | false | null | null | 0.35 |
8088d690c5e41e02896fb1339943e6b6c8e7326b3d440b93e8120bc11cb6cd36 | [
"arxiv",
"semantic_scholar"
] | CITRAS-FM: Tiny Time Series Foundation Model for Covariate-Informed Zero-Shot Forecasting | Pretrained time series foundation models (TSFMs) have enabled zero-shot forecasting on unseen target series. However, existing TSFMs often incur high computational cost and provide limited support for diverse variable types, often failing to account for covariates that exogenously influence target variability. To addre... | [
"Yosuke Yamaguchi",
"Issei Suemitsu",
"Yuki Kajihara",
"Wenpeng Wei"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2026-06-09T00:00:00 | https://arxiv.org/abs/2606.10798 | https://arxiv.org/pdf/2606.10798v1 | 2606.10798 | null | 0 | 0 | false | null | null | 0.35 |
338b17ff3356047227f22accabe19c2a1249a3343fbee77f6c8a3c9f827b3705 | [
"arxiv",
"semantic_scholar"
] | Does Normalization Choice Matter for Causal Large Time-Series Models? | Large models for time-series forecasting have been emerged as a promising paradigm for training models on heterogeneous collections of signals. These models typically rely on causal autoregressive architectures, where each observation is sequentially predicted from past. In practice, real-world time-series exhibit non-... | [
"Samy-Melwan Vilhes",
"Gilles Gasso",
"Mokhtar Z Alaya"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2026-06-08T00:00:00 | https://arxiv.org/abs/2606.09954 | https://arxiv.org/pdf/2606.09954v1 | 2606.09954 | null | 0 | 0 | false | null | ICLR 2026 Workshop: Time Series in the Age of Large Models, Apr 2026, Rio De Janeiro, Brazil | 0.55 |
235d0c175741471c5b415ad1bb73e7c101c55a1cd39cedda001777c5de06d76c | [
"arxiv",
"semantic_scholar"
] | Mix, Don't Pick: Why Synthetic Corpus Composition Matters for Time Series Foundation Model Pretraining | Choosing the wrong synthetic generator for time-series foundation model pretraining is costly: under identical training budgets, the best and worst generators produce up to a $2\times$ gap in forecasting error, yet the field has no principled way to make this choice. The problem is compounded by the fact that generator... | [
"Aaryan Nagpal",
"Debdeep Sanyal",
"Murari Mandal",
"Dhruv Kumar",
"Saurabh Deshpande"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2026-06-06T00:00:00 | https://arxiv.org/abs/2606.09912 | https://arxiv.org/pdf/2606.09912v1 | 2606.09912 | null | 0 | 0 | false | null | null | 0.35 |
a747331de032e2b3000c2385a2fac8e30e62765a27eebdec78b78fba179ccb06 | [
"arxiv",
"semantic_scholar"
] | Time series Foundation Models based on Physics-Informed Synthetic Histories for Cold-Start Photovoltaic Forecasting | At commissioning time, Photovoltaic (PV) operators must forecast production before target-site observations are available, limiting the direct use of standard supervised forecasters. This cold-start setting is addressed with a zero-shot pipeline that generates a synthetic production history from plant metadata and mete... | [
"Lorenzo Longarini",
"Alessandro Rongoni",
"Simone Silenzi",
"Emanuele Frontoni",
"Riccardo Rosati"
] | [
"cs.LG",
"eess.SP",
"stat.ML"
] | [
"Computer Science",
"Engineering",
"Mathematics"
] | 2026-06-05T00:00:00 | https://arxiv.org/abs/2606.07457 | https://arxiv.org/pdf/2606.07457v1 | 2606.07457 | null | 0 | 0 | false | null | null | 0.35 |
23b3adf0a3090f80017f25e8d8d9b835f2752e86425120830ee7c49ebb816084 | [
"arxiv",
"semantic_scholar"
] | GlucoFM-Bench: Benchmarking Time-Series Foundation Models for Blood Glucose Forecasting | Blood glucose forecasting models are foundational for modern diabetes management systems, as reliable short-term predictions can enable proactive interventions, support automated insulin delivery, and reduce the risk of hypo- and hyperglycemic events. From a modeling perspective, glucose forecasting poses unique challe... | [
"Baiying Lu",
"Zhaohui Liang",
"Ryan Pontius",
"Shengpu Tang",
"Temiloluwa Prioleau"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2026-06-05T00:00:00 | https://arxiv.org/abs/2606.06881 | https://arxiv.org/pdf/2606.06881v1 | 2606.06881 | null | 0 | 0 | false | null | null | 0.35 |
eb11c82d98ce0c13ee337edf97a2693116969666b120b5f336a40d3558faa8ee | [
"arxiv",
"semantic_scholar"
] | TS-ICL: A Flexible Time-Indexed Foundation Model for Time Series via In-Context Learning | Foundation models mark a profound paradigm shift in time series modeling, with task-specific models being superseded by general-purpose zero-shot models. Yet, current approaches primarily focus on forecasting, while real-world time series are often irregularly and partially observed, requiring models that can jointly f... | [
"Etienne Le Naour",
"Tahar Nabil",
"Adrien Petralia"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2026-06-04T00:00:00 | https://arxiv.org/abs/2606.05878 | https://arxiv.org/pdf/2606.05878v2 | 2606.05878 | null | 0 | 0 | false | null | null | 0.35 |
d198453f3a21e5f3765bc67b8838c8038da4117b5efce210f803d4e8195457c6 | [
"arxiv",
"semantic_scholar"
] | TimeBlocks: Foundational and Continual Time-Series Blockbase -- Extended Version | The ongoing digitization has led to a proliferation of time-series data streams that monitor a variety of processes, from which valuable insights may be obtained. Further, the emergence of successful foundational language models begs the question of whether it is possible to achieve time-series models with the foundati... | [
"David Campos",
"Bin Yang",
"Tung Kieu",
"Lei Chen",
"Chenjuan Guo",
"Christian S. Jensen"
] | [
"cs.LG",
"cs.DB"
] | [
"Computer Science"
] | 2026-06-01T00:00:00 | https://arxiv.org/abs/2606.02142 | https://arxiv.org/pdf/2606.02142v1 | 2606.02142 | null | 0 | 0 | false | null | null | 0.35 |
33e34abccdbaaa3dfcb78a5a9c9d1c7b1e18961dfefeccc40708f4dfd166dba4 | [
"arxiv",
"semantic_scholar"
] | Time Series as Language: A Universal Tokenizer for General-Purpose Time Series Foundation Models | While Next-Token Prediction (NTP) has unified LLM pretraining, its adaptation to unbounded, continuous time series (TS) remains open. To bridge the gap, we introduce UniTok, a universal tokenizer that transforms TS into discrete tokens, and UniTok-FM, a foundation model pretrained via NTP on these tokens. UniTok-FM is ... | [
"Yunhao Zhang",
"Ruiying Qi",
"Jiale Zheng",
"Jianfeng Zhang",
"Lujia Pan",
"Junchi Yan"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2026-05-31T00:00:00 | https://arxiv.org/abs/2606.09861 | https://arxiv.org/pdf/2606.09861v1 | 2606.09861 | null | 0 | 0 | false | null | null | 0.35 |
9e2edd3a20c3a730700b93873a85bbbdd9f05766af38fca8a5891b7b01134163 | [
"arxiv",
"semantic_scholar"
] | KairosAgent: Agentic Time Series Forecasting with Fused Semantic Reasoning | Cross-domain multimodal time series forecasting is a challenging task, requiring models to integrate precise numerical comprehension, cross-domain semantic understanding, and effective multimodal fusion. Existing approaches either build Time Series Foundation Models (TSFMs) from scratch or leverage pretrained Large Lan... | [
"Kun Feng",
"Ziwei Shan",
"Yuchen Fang",
"Yiyang Tan",
"Sihan Lu",
"Shuqi Gu",
"Lintao Ma",
"Xingyu Lu",
"Kan Ren"
] | [
"cs.AI"
] | [
"Computer Science"
] | 2026-05-28T00:00:00 | https://arxiv.org/abs/2605.30002 | https://arxiv.org/pdf/2605.30002v1 | 2605.30002 | null | 0 | 0 | false | null | null | 0.35 |
e4b2536b0fe4f721202b1ac28b188887b92fcec60eec13ffeac945e8035a6138 | [
"arxiv",
"semantic_scholar"
] | AME-TS: Anchored Mixture-of-Experts for Time Series Forecasting | Time series forecasting models are increasingly scaled through large Transformer backbones, yet most existing approaches process all series through a shared dense computation path despite substantial heterogeneity in temporal structure. Mixture-of-Experts (MoE) offers a natural alternative by enabling conditional compu... | [
"Rui Wang",
"Renhao Xue",
"Ray Razi",
"Huan Song",
"Hannah R. Marlowe"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2026-05-24T00:00:00 | https://arxiv.org/abs/2605.25166 | https://arxiv.org/pdf/2605.25166v1 | 2605.25166 | null | 0 | 0 | false | null | null | 0.35 |
816bfc39be3b916acf16044a560eb8a681f21766318e82d5a9b1e9d094bd5761 | [
"arxiv",
"semantic_scholar"
] | Assessing the Operational Viability of Foundation Models for Time Series Forecasting | Time series forecasting drives operational decisions in areas like finance, transportation, and energy. While supervised learning approaches achieve strong performance, they require domain-specific training, feature engineering, and ongoing maintenance. Large-scale foundation models have recently emerged as a zero-shot... | [
"Kavin Soni",
"Debanshu Das",
"Vamshi Guduguntla"
] | [
"cs.LG",
"cs.AI",
"stat.AP",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2026-05-23T00:00:00 | https://arxiv.org/abs/2605.24381 | https://arxiv.org/pdf/2605.24381v1 | 2605.24381 | null | 0 | 0 | true | https://github.com/kavin-soni/timeseries-zeroshot-eval | null | 0.65 |
5d614b06036759653547d79410ec8970a9efaff83306926ed4955677c93430fc | [
"arxiv"
] | Chronicle: A Multimodal Foundation Model for Joint Language and Time Series Understanding | Real-world time series come with text: metadata, descriptions, news, reports. Yet time series foundation models process numerical sequences in isolation, and the multimodal text-and-time-series models that attempt to bridge the two all adapt a pretrained language model post hoc, inheriting representations shaped withou... | [
"Paul Quinlan",
"Jeremy Levasseur",
"Qingguo Li",
"Xiaodan Zhu"
] | [
"cs.LG",
"cs.AI",
"cs.CL"
] | [] | 2026-05-18T00:00:00 | https://arxiv.org/abs/2605.20268 | https://arxiv.org/pdf/2605.20268v1 | 2605.20268 | null | 0 | 0 | false | null | null | 0.35 |
96eba68f54fe844378f53c7f54d1eadffd814011a9e5839a3a50edb790a04f76 | [
"arxiv",
"semantic_scholar"
] | Empirical evaluation of Time Series Foundation Models for Day-ahead and Imbalance Electricity Price Forecasting in Belgium | Recent advances in Time Series Foundation Models (TSFMs) promise zero-shot forecasting capabilities with minimal task-specific training. While these models have shown strong performance across generic benchmarks, their applicability in volatile, complex electricity markets remains underexplored. Addressing this gap, th... | [
"Chi Bui",
"Maria Margarida Mascarenhas",
"Arnaud Verstraeten",
"Hussain Kazmi"
] | [
"eess.SY",
"cs.LG"
] | [
"Engineering",
"Computer Science"
] | 2026-05-16T00:00:00 | https://arxiv.org/abs/2605.17045 | https://arxiv.org/pdf/2605.17045v1 | 2605.17045 | null | 0 | 0 | false | null | null | 0.35 |
4d47028b83a40d304b240a66abfd251dc54660ac5f485f8f02dcbda8592f01b8 | [
"arxiv",
"semantic_scholar"
] | EHR-RAGp: Retrieval-Augmented Prototype-Guided Foundation Model for Electronic Health Records | Electronic Health Records (EHR) contain rich longitudinal patient information and are widely used in predictive modeling applications. However, effectively leveraging historical data remains challenging due to long trajectories, heterogeneous events, temporal irregularity, and the varying relevance of past clinical con... | [
"Saeed Shurrab",
"Mariam Al-Omari",
"Dana El Samad",
"Farah E. Shamout"
] | [
"cs.IR",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2026-05-12T00:00:00 | https://arxiv.org/abs/2605.12335 | https://arxiv.org/pdf/2605.12335v1 | 2605.12335 | null | 0 | 0 | false | null | null | 0.35 |
e3f48b98fdd1bbc2c2857608fabbd634a35dbb7a70a4340d8d15802143e361f1 | [
"arxiv",
"semantic_scholar"
] | Benchmarking Transformer and xLSTM for Time-Series Forecasting of Heat Consumption | Obtaining an accurate short-term forecasting for heat demand is an essential part of operating district heating networks cost-efficient and reliable. Heat consumption time series at the building level are highly dependent on exogenous variables such as outdoor temperature and individual usage patterns, making forecasti... | [
"Marja Wahl",
"Daniel R. Bayer",
"Sven Rausch",
"Marco Pruckner"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2026-05-10T00:00:00 | https://arxiv.org/abs/2605.09722 | https://arxiv.org/pdf/2605.09722v1 | 2605.09722 | 10.1109/SusTech67720.2026.11536443 | 1 | 0 | false | null | IEEE Conference on Technologies for Sustainability | 0.55 |
48fb1a04cfec538439d88a9420f88d9f66df4a1053e6897dddb6060cc4033852 | [
"arxiv",
"semantic_scholar"
] | Multivariate Financial Forecasting using the Chronos Time Series Foundation Models | Using Chronos-2, an open-source time-series foundation model, we evaluate pretrained time-series models for economic and financial forecasting with an emphasis on whether multivariate (MV) inputs improve accuracy relative to univariate (UV) baselines. The study covers two panels -- the Magnificent-7 equities and U.S. T... | [
"Sanjiv R Das",
"Tarang Goyal",
"Mohini Yadav"
] | [
"q-fin.ST",
"cs.AI"
] | [
"Economics",
"Computer Science"
] | 2026-05-08T00:00:00 | https://arxiv.org/abs/2605.21504 | https://arxiv.org/pdf/2605.21504v1 | 2605.21504 | null | 0 | 0 | true | null | null | 0.65 |
a45dff8ddca734b1b4f792e41c4e78b95d040527068a14536c5d96b278068aa0 | [
"arxiv",
"semantic_scholar"
] | Superposition Is Not Necessary: A Mechanistic Interpretability Analysis of Transformer Representations for Time Series Forecasting | Transformer architectures have been widely adopted for time series forecasting, yet whether the representational mechanisms that make them powerful in NLP actually engage on time series data remains unexplored. The persistent competitiveness of simple linear models such as DLinear has fueled ongoing debate, but no mech... | [
"Alper YΔ±ldΔ±rΔ±m"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2026-05-06T00:00:00 | https://arxiv.org/abs/2605.05151 | https://arxiv.org/pdf/2605.05151v1 | 2605.05151 | null | 0 | 0 | false | null | null | 0.35 |
9d82239cda9829f10466586c6f12f894193745919af62e50a5cbff830bb30a44 | [
"arxiv",
"semantic_scholar"
] | Explainable Load Forecasting with Covariate-Informed Time Series Foundation Models | Time Series Foundation Models (TSFMs) have recently emerged as general-purpose forecasting models and show considerable potential for applications in energy systems. However, applications in critical infrastructure like power grids require transparency to ensure trust and reliability and cannot rely on pure black-box m... | [
"Matthias Hertel",
"Alexandra Nikoltchovska",
"Sebastian PΓΌtz",
"Ralf Mikut",
"Benjamin SchΓ€fer",
"Veit Hagenmeyer"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2026-04-30T00:00:00 | https://arxiv.org/abs/2604.28149 | https://arxiv.org/pdf/2604.28149v1 | 2604.28149 | 10.1145/3744255.3811724 | 0 | 0 | false | null | null | 0.35 |
74819adb3eb194673d10944f2c79a8cad2c5672ad301233d4ecb97cb761096f2 | [
"arxiv",
"semantic_scholar"
] | FETS Benchmark: Foundation Models Outperform Dataset-specific Machine Learning in Energy Time Series Forecasting | Driven by the transition towards a climate-neutral energy system, accurate energy time series forecasting is critical for planning and operation. Yet, it remains largely a dataset-specific task, requiring comprehensive training data, limiting scalability, and resulting in high model development and maintenance effort. ... | [
"Marco Obermeier",
"Marco Pruckner",
"Florian Haselbeck",
"Andreas Zeiselmair"
] | [
"cs.LG",
"cs.AI",
"cs.CE"
] | [
"Computer Science"
] | 2026-04-24T00:00:00 | https://arxiv.org/abs/2604.22328 | https://arxiv.org/pdf/2604.22328v1 | 2604.22328 | 10.48550/arXiv.2604.22328 | 0 | 0 | false | null | arXiv.org | 0.55 |
dfb96aa68551405b0f376ad5ffb264c5e647e2ed9a243d63341d3faf496da722 | [
"arxiv",
"semantic_scholar"
] | Empirical Assessment of Time-Series Foundation Models For Power System Forecasting Applications | Accurate forecasting of electric load and renewable generation is essential for reliable and cost effective power system operations. Recent advances in transformer based and foundation machine learning models, driven by large scale pretraining, increased available data and computation, in addition to architectural inno... | [
"Muhy Eddin Za'ter",
"Bri-Mathias Hodge"
] | [
"eess.SY"
] | [
"Computer Science",
"Engineering"
] | 2026-04-23T00:00:00 | https://arxiv.org/abs/2604.22077 | https://arxiv.org/pdf/2604.22077v1 | 2604.22077 | 10.48550/arXiv.2604.22077 | 0 | 0 | false | null | arXiv.org | 0.55 |
ec54466fe7741e99b979b1413d921dfdb2cbe8ec73297d1c7cee8eeb2eac723c | [
"arxiv",
"semantic_scholar"
] | WaveMoE: A Wavelet-Enhanced Mixture-of-Experts Foundation Model for Time Series Forecasting | Time series foundation models (TSFMs) have recently achieved remarkable success in universal forecasting by leveraging large-scale pretraining on diverse time series data. Complementing this progress, incorporating frequency-domain information yields promising performance in enhancing the modeling of complex temporal p... | [
"Shunyu Wu",
"Jiawei Huang",
"Weibin Feng",
"Boxin Li",
"Xiao Zhang",
"Erli Meng",
"Dan Li",
"Jian Lou",
"See-Kiong Ng"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2026-04-12T00:00:00 | https://arxiv.org/abs/2604.10544 | https://arxiv.org/pdf/2604.10544v1 | 2604.10544 | 10.48550/arXiv.2604.10544 | 0 | 0 | false | null | arXiv.org | 0.5408 |
9508de7e24e50eba0c8bbe7e149cb1ec7e417cb770b4b29759a9c8e4e018bde7 | [
"arxiv",
"semantic_scholar"
] | Zero-shot Multivariate Time Series Forecasting Using Tabular Prior Fitted Networks | Tabular foundation models, particularly Prior-data Fitted Networks like TabPFN have emerged as the leading contender in a myriad of tasks ranging from data imputation to label prediction on the tabular data format surpassing the historical successes of tree-based models. This has led to investigations on their applicab... | [
"Mayuka Jayawardhana",
"Nihal Sharma",
"Kazem Meidani",
"Bayan Bruss",
"Tom Goldstein",
"Doron Bergman"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2026-04-09T00:00:00 | https://arxiv.org/abs/2604.08400 | https://arxiv.org/pdf/2604.08400v1 | 2604.08400 | 10.48550/arXiv.2604.08400 | 0 | 0 | false | null | arXiv.org | 0.5374 |
4c443abf1ee6741bcbfdfbeb68da9cb192d4997791082e96c70b218ed9f7c621 | [
"arxiv",
"semantic_scholar"
] | Dynamic Linear Coregionalization for Realistic Synthetic Multivariate Time Series | Synthetic data is essential for training foundation models for time series (FMTS), but most generators assume static correlations, and are typically missing realistic inter-channel dependencies. We introduce DynLMC, a Dynamic Linear Model of Coregionalization, that incorporates time-varying, regime-switching correlatio... | [
"Annita Vapsi",
"Penghang Liu",
"Saheed Obitayo",
" Aakriti",
"Manoj Cherukumalli",
"Prathamesh Patil",
"Amit Varshney",
"Nicolas Marchesotti",
"Elizabeth Fons",
"Vamsi K. Potluru",
"Manuela Veloso"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2026-04-06T00:00:00 | https://arxiv.org/abs/2604.05064 | https://arxiv.org/pdf/2604.05064v2 | 2604.05064 | 10.48550/arXiv.2604.05064 | 0 | 0 | false | null | arXiv.org | 0.534 |
17393d7a2fc863455b515f0b452bc60a87f330c5315153952d06cbba33b5afd0 | [
"arxiv",
"semantic_scholar"
] | Forecast collapse of transformer-based models under squared loss in financial time series | We study trajectory forecasting under squared loss for time series with weak conditional structure, using highly expressive prediction models. Building on the classical characterization of squared-loss risk minimization, we emphasize regimes in which the conditional expectation of future trajectories is effectively deg... | [
"Pierre Andreoletti"
] | [
"stat.ML",
"cs.LG",
"math.PR",
"math.ST",
"q-fin.CP"
] | [
"Computer Science",
"Mathematics",
"Economics"
] | 2026-03-31T00:00:00 | https://arxiv.org/abs/2604.00064 | https://arxiv.org/pdf/2604.00064v1 | 2604.00064 | 10.48550/arXiv.2604.00064 | 0 | 0 | false | null | arXiv.org | 0.5271 |
1897a463760b10ae19dd5deb41a1b962ac5044b2947ece095bc226440f5682ab | [
"arxiv",
"semantic_scholar"
] | Multimodal Forecasting for Commodity Prices Using Spectrogram-Based and Time Series Representations | Forecasting multivariate time series remains challenging due to complex cross-variable dependencies and the presence of heterogeneous external influences. This paper presents Spectrogram-Enhanced Multimodal Fusion (SEMF), which combines spectral and temporal representations for more accurate and robust forecasting. The... | [
"Soyeon Park",
"Doohee Chung",
"Charmgil Hong"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2026-03-28T00:00:00 | https://arxiv.org/abs/2603.27321 | https://arxiv.org/pdf/2603.27321v1 | 2603.27321 | 10.48550/arXiv.2603.27321 | 0 | 0 | false | null | arXiv.org | 0.5236 |
804c75bbe5ac8f335b64711994778ec81f85e78b76dc78c14ba8f93ef0f61061 | [
"arxiv",
"semantic_scholar"
] | QuitoBench: A High-Quality Open Time Series Forecasting Benchmark | Time series forecasting is critical across finance, healthcare, and cloud computing, yet progress is constrained by a fundamental bottleneck: the scarcity of large-scale, high-quality benchmarks. To address this gap, we introduce \textsc{QuitoBench}, a regime-balanced benchmark for time series forecasting with coverage... | [
"Siqiao Xue",
"Zhaoyang Zhu",
"Wei Zhang",
"Rongyao Cai",
"Rui Wang",
"Yixiang Mu",
"Fan Zhou",
"Jianguo Li",
"Peng Di",
"Hang Yu"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2026-03-27T00:00:00 | https://arxiv.org/abs/2603.26017 | https://arxiv.org/pdf/2603.26017v1 | 2603.26017 | 10.48550/arXiv.2603.26017 | 4 | 0 | true | null | arXiv.org | 0.8075 |
9edf58766f9ca387bcccb8347cbea5e82ef6baacd508a6008214584018edbec1 | [
"arxiv",
"semantic_scholar"
] | IPatch: A Multi-Resolution Transformer Architecture for Robust Time-Series Forecasting | Accurate forecasting of multivariate time series remains challenging due to the need to capture both short-term fluctuations and long-range temporal dependencies. Transformer-based models have emerged as a powerful approach, but their performance depends critically on the representation of temporal data. Traditional po... | [
"Aymane Harkati",
"Moncef Garouani",
"Olivier Teste",
"Julien Aligon",
"Mohamed Hamlich"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2026-03-25T00:00:00 | https://arxiv.org/abs/2603.24207 | https://arxiv.org/pdf/2603.24207v1 | 2603.24207 | 10.48550/arXiv.2603.24207 | 0 | 0 | false | null | arXiv.org | 0.5202 |
df8309b1f27e72fb87c2fb10f663336f22191772d07d0ed64e12ba0d6dee3ba8 | [
"arxiv",
"semantic_scholar"
] | Forecasting with Guidance: Representation-Level Supervision for Time Series Forecasting | Nowadays, time series forecasting is predominantly approached through the end-to-end training of deep learning architectures using error-based objectives. While this is effective at minimizing average loss, it encourages the encoder to discard informative yet extreme patterns. This results in smooth predictions and tem... | [
"Jiacheng Wang",
"Liang Fan",
"Baihua Li",
"Luyan Zhang"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2026-03-25T00:00:00 | https://arxiv.org/abs/2603.24262 | https://arxiv.org/pdf/2603.24262v1 | 2603.24262 | 10.48550/arXiv.2603.24262 | 0 | 0 | false | null | arXiv.org | 0.5202 |
7670097b8e92b6505edfd80fb410cde87c0f5aa48a1efb2b2b0b274006ec5be4 | [
"arxiv",
"semantic_scholar"
] | CoRA: Boosting Time Series Foundation Models for Multivariate Forecasting through Correlation-aware Adapter | Most existing Time Series Foundation Models (TSFMs) use channel independent modeling and focus on capturing and generalizing temporal dependencies, while neglecting the correlations among channels or overlooking the different aspects of correlations. However, these correlations play a vital role in Multivariate time se... | [
"Hanyin Cheng",
"Xingjian Wu",
"Yang Shu",
"Zhongwen Rao",
"Lujia Pan",
"Bin Yang",
"Chenjuan Guo"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2026-03-23T00:00:00 | https://arxiv.org/abs/2603.21828 | https://arxiv.org/pdf/2603.21828v1 | 2603.21828 | 10.48550/arXiv.2603.21828 | 2 | 0 | false | null | arXiv.org | 0.5179 |
15b22151dba2c0c6384838fcc8e08c2a456abbbf287962dfafbab4e0fb386d65 | [
"arxiv",
"semantic_scholar"
] | A Foundation Model for Instruction-Conditioned In-Context Time Series Tasks | In-context learning (ICL) enables task adaptation at inference time by conditioning on demonstrations rather than updating model parameters. Although recent time-series foundation models incorporate contextual conditioning, retrieval, or example-based prompting, they typically rely on implicit positional structure or t... | [
"Anish Saha",
"Konstantin Shmakov"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2026-03-23T00:00:00 | https://arxiv.org/abs/2603.22586 | https://arxiv.org/pdf/2603.22586v3 | 2603.22586 | 10.48550/arXiv.2603.22586 | 0 | 0 | false | null | arXiv.org | 0.5179 |
f4a397fc6e2b9a9ae84cb89bd66c0ec5435b02a721ce2c065d30feea8940b4a4 | [
"arxiv",
"semantic_scholar"
] | FISformer: Replacing Self-Attention with a Fuzzy Inference System in Transformer Models for Time Series Forecasting | Transformers have achieved remarkable progress in time series forecasting, yet their reliance on deterministic dot-product attention limits their capacity to model uncertainty and nonlinear dependencies across multivariate temporal dimensions. To address this limitation, we propose FISFormer, a Fuzzy Inference System-d... | [
"Bulent Haznedar",
"Levent Karacan"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2026-03-23T00:00:00 | https://arxiv.org/abs/2603.21724 | https://arxiv.org/pdf/2603.21724v1 | 2603.21724 | 10.1109/tfuzz.2026.3690012 | 0 | 0 | false | null | IEEE transactions on fuzzy systems | 0.5179 |
ff57824304777f9714ec230d3f8b234041a2234d5466e58ce161aab76fbee0bf | [
"arxiv",
"semantic_scholar"
] | Integrating Inductive Biases in Transformers via Distillation for Financial Time Series Forecasting | Transformer-based models have been widely adopted for time-series forecasting due to their high representational capacity and architectural flexibility. However, many Transformer variants implicitly assume stationarity and stable temporal dynamics -- assumptions routinely violated in financial markets characterized by ... | [
"Yu-Chen Den",
"Kuan-Yu Chen",
"Kendro Vincent",
"Darby Tien-Hao Chang"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2026-03-17T00:00:00 | https://arxiv.org/abs/2603.16985 | https://arxiv.org/pdf/2603.16985v2 | 2603.16985 | 10.48550/arXiv.2603.16985 | 0 | 0 | false | null | arXiv.org | 0.511 |
1b056a184b59695aa9b66441e253390dcb0b00522c7178533f96303fa69f9574 | [
"arxiv",
"semantic_scholar"
] | Not All Retrievals are Useful: Cross-Attention for Input-Aware RAG in Time Series Forecasting | Retrieval-augmented generation (RAG) enhances zero-shot time series (TS) forecasting by leveraging external knowledge bases, yet existing approaches overlook input-level relevance when fusing retrieved samples with the query. We argue that not all retrievals are equally useful, and irrelevant ones can degrade performan... | [
"Seunghan Lee",
"Jaehoon Lee",
"Jun Seo",
"Sungdong Yoo",
"Minjae Kim",
"Tae Yoon Lim",
"Dongwan Kang",
"Hwanil Choi",
"SoonYoung Lee",
"Wonbin Ahn"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2026-03-16T00:00:00 | https://arxiv.org/abs/2603.14709 | https://arxiv.org/pdf/2603.14709v2 | 2603.14709 | null | 1 | 1 | true | https://github.com/seunghan96/cross-rag/ | null | 0.6026 |
bc258b366c67b84e16aae670e16334fa8aa92cfbb94d5caac33fe94bd82f4d69 | [
"arxiv",
"semantic_scholar"
] | Interventional Time Series Priors for Causal Foundation Models | Prior-data fitted networks (PFNs) have emerged as powerful foundation models for tabular causal inference, yet their extension to time series remains limited by the absence of synthetic data generators that provide interventional targets. Existing time series benchmarks generate observational data with ground-truth cau... | [
"Dennis Thumm",
"Ying Chen"
] | [
"cs.LG",
"stat.ME"
] | [
"Computer Science",
"Mathematics"
] | 2026-03-11T00:00:00 | https://arxiv.org/abs/2603.11090 | https://arxiv.org/pdf/2603.11090v2 | 2603.11090 | 10.48550/arXiv.2603.11090 | 2 | 0 | false | null | arXiv.org | 0.5042 |
2550e7f22cdcf4fe727f72bf13decd0373bbb1910ad4c840fdb88bd9534e1138 | [
"arxiv",
"semantic_scholar"
] | Dissecting Chronos: Sparse Autoencoders Reveal Causal Feature Hierarchies in Time Series Foundation Models | Time series foundation models (TSFMs) are increasingly deployed in high-stakes domains, yet their internal representations remain opaque. We present the first application of sparse autoencoders (SAEs) to a TSFM, training TopK SAEs on activations of Chronos-T5-Large (710M parameters) across six layers. Through 392 singl... | [
"Anurag Mishra"
] | [
"cs.LG",
"cs.AI",
"cs.CL"
] | [
"Computer Science"
] | 2026-03-10T00:00:00 | https://arxiv.org/abs/2603.10071 | https://arxiv.org/pdf/2603.10071v1 | 2603.10071 | 10.48550/arXiv.2603.10071 | 1 | 0 | false | null | arXiv.org | 0.503 |
e693f36328b5b2db3ce9e224bcef260977c1a4375a1ee36756be1cd112d8ec2a | [
"arxiv",
"semantic_scholar"
] | UTICA: Multi-Objective Self-Distllation Foundation Model Pretraining for Time Series Classification | Self-supervised foundation models have achieved remarkable success across domains, including time series. However, the potential of non-contrastive methods, a paradigm that has driven significant advances in computer vision, remains underexplored for time series. In this work, we adapt DINOv2-style self-distillation to... | [
"Yessin Moakher",
"Youssef Attia El Hili",
"Vasilii Feofanov"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2026-03-02T00:00:00 | https://arxiv.org/abs/2603.01348 | https://arxiv.org/pdf/2603.01348v1 | 2603.01348 | 10.48550/arXiv.2603.01348 | 1 | 0 | false | null | arXiv.org | 0.4939 |
0c737fdb767a4f7d6bd12d5d3a319506b95227b0bf8929a988570afb1bcacf00 | [
"arxiv",
"semantic_scholar"
] | Retrodictive Forecasting: A Proof-of-Concept for Exploiting Temporal Asymmetry in Time Series Prediction | We propose a retrodictive forecasting paradigm for time series: instead of predicting the future from the past, we identify the future that best explains the observed present via inverse MAP optimization over a Conditional Variational Autoencoder (CVAE). This conditioning is a statistical modeling choice for Bayesian i... | [
"Cedric Damour"
] | [
"cs.LG",
"physics.ao-ph",
"stat.ML"
] | [
"Computer Science",
"Physics",
"Mathematics"
] | 2026-02-28T00:00:00 | https://arxiv.org/abs/2603.00636 | https://arxiv.org/pdf/2603.00636v1 | 2603.00636 | 10.48550/arXiv.2603.00636 | 0 | 0 | true | https://github.com/cdamour/retrodictive-forecasting | arXiv.org | 0.7597 |
5e6b55a3de03c005894ad5e864197de0f2a85193d45c2607e9519a2f4ad32517 | [
"arxiv",
"semantic_scholar"
] | Time Series Foundation Models as Strong Baselines in Transportation Forecasting: A Large-Scale Benchmark Analysis | Accurate forecasting of transportation dynamics is essential for urban mobility and infrastructure planning. Although recent work has achieved strong performance with deep learning models, these methods typically require dataset-specific training, architecture design and hyper-parameter tuning. This paper evaluates whe... | [
"Javier Yanes-Pulido",
"Filipe Rodrigues"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2026-02-27T00:00:00 | https://arxiv.org/abs/2602.24238 | https://arxiv.org/pdf/2602.24238v2 | 2602.24238 | 10.48550/arXiv.2602.24238 | 1 | 0 | false | null | arXiv.org | 0.4904 |
51f7d85bf74586edc7e24b1e01f7412331bece504ebf0f48047ee242ec473c4d | [
"arxiv",
"semantic_scholar"
] | DualWeaver: Synergistic Feature Weaving Surrogates for Multivariate Forecasting with Univariate Time Series Foundation Models | Time-series foundation models (TSFMs) have achieved strong univariate forecasting through large-scale pre-training, yet effectively extending this success to multivariate forecasting remains challenging. To address this, we propose DualWeaver, a novel framework that adapts univariate TSFMs (Uni-TSFMs) for multivariate ... | [
"Jinpeng Li",
"Zhongyi Pei",
"Huaze Xue",
"Bojian Zheng",
"Chen Wang",
"Jianmin Wang"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2026-02-25T00:00:00 | https://arxiv.org/abs/2602.22066 | https://arxiv.org/pdf/2602.22066v1 | 2602.22066 | 10.48550/arXiv.2602.22066 | 0 | 0 | true | https://github.com/li-jinpeng/DualWeaver | arXiv.org | 0.7544 |
66985c4508b91a1723f5b2359d6e6222afe87deda0a0175fcc39f1730020f928 | [
"arxiv",
"semantic_scholar"
] | Reverso: Efficient Time Series Foundation Models for Zero-shot Forecasting | Learning time series foundation models has been shown to be a promising approach for zero-shot time series forecasting across diverse time series domains. Insofar as scaling has been a critical driver of performance of foundation models in other modalities such as language and vision, much recent work on time series fo... | [
"Xinghong Fu",
"Yanhong Li",
"Georgios Papaioannou",
"Yoon Kim"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2026-02-19T00:00:00 | https://arxiv.org/abs/2602.17634 | https://arxiv.org/pdf/2602.17634v1 | 2602.17634 | 10.48550/arXiv.2602.17634 | 3 | 1 | false | null | arXiv.org | 0.4813 |
80275cfa12ac0fd461284c2ab694729311d10e60598387b2751c5eee1076d5cd | [
"arxiv",
"semantic_scholar"
] | Structure-Aware Set Transformers: Temporal and Variable-Type Attention Biases for Asynchronous Clinical Time Series | Electronic health records (EHR) are irregular, asynchronous multivariate time series. As time-series foundation models increasingly tokenize events rather than discretizing time, the input layout becomes a key design choice. Grids expose time$\times$variable structure but require imputation or missingness masks, riskin... | [
"Joohyung Lee",
"Kwanhyung Lee",
"Changhun Kim",
"Eunho Yang"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2026-02-18T00:00:00 | https://arxiv.org/abs/2603.06605 | https://arxiv.org/pdf/2603.06605v2 | 2603.06605 | 10.48550/arXiv.2603.06605 | 0 | 0 | false | null | arXiv.org | 0.4801 |
849683763fdb23eed49854d8333921e61adcbe6b41c423a4a47108228172da98 | [
"arxiv",
"semantic_scholar"
] | EIDOS: Latent-Space Predictive Learning for Time Series Foundation Models | Most time series foundation models are pretrained by directly predicting future observations, which often yields weakly structured latent representations that capture surface noise rather than coherent and predictable temporal dynamics. In this work, we introduce EIDOS, a foundation model family that shifts pretraining... | [
"Xinxing Zhou",
"Qingren Yao",
"Yiji Zhao",
"Chenghao Liu",
"Flora Salim",
"Xiaojie Yuan",
"Yanlong Wen",
"Ming Jin"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2026-02-15T00:00:00 | https://arxiv.org/abs/2602.14024 | https://arxiv.org/pdf/2602.14024v1 | 2602.14024 | 10.48550/arXiv.2602.14024 | 0 | 0 | false | null | arXiv.org | 0.4767 |
c0afc7849df6d894effc0b8f4c76ddc1c729ab13d24779ba9775458303262d23 | [
"arxiv",
"semantic_scholar"
] | Forecasting Commencing Enrolments Under Data Sparsity: A Zero-Shot Time Series Foundation Models Framework for Higher Education Planning | Effective resource allocation in higher education depends on reliable enrolment forecasts, yet institutional planners frequently face data series disrupted by structural shifts. This paper investigates whether zero-shot Time Series Foundation Models (TSFMs) can provide rigorous decision support for annual enrolment for... | [
"Jittarin Jetwiriyanon",
"Teo Susnjak",
"Surangika Ranathunga"
] | [
"cs.AI"
] | [
"Computer Science"
] | 2026-02-12T00:00:00 | https://arxiv.org/abs/2602.12120 | https://arxiv.org/pdf/2602.12120v3 | 2602.12120 | null | 0 | 0 | false | null | null | 0.3011 |
daed730a2d8e299725fbe5052cb0b7608e1f468cdc721b9cb60c6e9519d57ec9 | [
"arxiv",
"semantic_scholar"
] | DiTS: Multimodal Diffusion Transformers Are Time Series Forecasters | While generative modeling on time series facilitates more capable and flexible probabilistic forecasting, existing generative time series models do not address the multi-dimensional properties of time series data well. The prevalent architecture of Diffusion Transformers (DiT), which relies on simplistic conditioning c... | [
"Haoran Zhang",
"Haixuan Liu",
"Yong Liu",
"Yunzhong Qiu",
"Yuxuan Wang",
"Jianmin Wang",
"Mingsheng Long"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2026-02-06T00:00:00 | https://arxiv.org/abs/2602.06597 | https://arxiv.org/pdf/2602.06597v1 | 2602.06597 | 10.48550/arXiv.2602.06597 | 1 | 0 | false | null | arXiv.org | 0.4664 |
b9610e1d14ba1a6dc3f64b169e68b1bd492c03d26bbb4470aecb41e0f5e3c44c | [
"arxiv",
"semantic_scholar"
] | Revisiting the Generic Transformer: Deconstructing a Strong Baseline for Time Series Foundation Models | The recent surge in Time Series Foundation Models has rapidly advanced the field, yet the heterogeneous training setups across studies make it difficult to attribute improvements to architectural innovations versus data engineering. In this work, we investigate the potential of a standard patch Transformer, demonstrati... | [
"Yunshi Wen",
"Wesley M. Gifford",
"Chandra Reddy",
"Lam M. Nguyen",
"Jayant Kalagnanam",
"Anak Agung Julius"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2026-02-06T00:00:00 | https://arxiv.org/abs/2602.06909 | https://arxiv.org/pdf/2602.06909v1 | 2602.06909 | 10.48550/arXiv.2602.06909 | 2 | 1 | true | null | arXiv.org | 0.7207 |
858ee827475c15707ff29aa79b5cf892eaecb84ce81bc1e396fc05176a51d215 | [
"arxiv",
"semantic_scholar"
] | Assessing Electricity Demand Forecasting with Exogenous Data in Time Series Foundation Models | Time-series foundation models have emerged as a new paradigm for forecasting, yet their ability to effectively leverage exogenous features -- critical for electricity demand forecasting -- remains unclear. This paper empirically evaluates foundation models capable of modeling cross-channel correlations against a baseli... | [
"Wei Soon Cheong",
"Lian Lian Jiang",
"Jamie Ng Suat Ling"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2026-02-05T00:00:00 | https://arxiv.org/abs/2602.05390 | https://arxiv.org/pdf/2602.05390v1 | 2602.05390 | 10.48550/arXiv.2602.05390 | 0 | 0 | false | null | arXiv.org | 0.4652 |
dd9b72e790ea93834e75a536993cbf70560cdd5923a8a00c3ab34791e1020644 | [
"arxiv",
"semantic_scholar"
] | Position: Beyond Model-Centric Prediction -- Agentic Time Series Forecasting | Time series forecasting has traditionally been formulated as a model-centric, static, and single-pass prediction problem that maps historical observations to future values. While this paradigm has driven substantial progress, it proves insufficient in adaptive and multi-turn settings where forecasting requires informat... | [
"Mingyue Cheng",
"Xiaoyu Tao",
"Qi Liu",
"Ze Guo",
"Enhong Chen"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2026-02-02T00:00:00 | https://arxiv.org/abs/2602.01776 | https://arxiv.org/pdf/2602.01776v4 | 2602.01776 | 10.48550/arXiv.2602.01776 | 7 | 1 | false | null | arXiv.org | 0.4618 |
dc664c75d9bc2170a8a48db9d32cc44c65f700e137c51a47a094ab5d996933be | [
"arxiv",
"semantic_scholar"
] | SEDformer: Event-Synchronous Spiking Transformers for Irregular Telemetry Time Series Forecasting | Telemetry streams from large-scale Internet-connected systems (e.g., IoT deployments and online platforms) naturally form an irregular multivariate time series (IMTS) whose accurate forecasting is operationally vital. A closer examination reveals a defining Sparsity-Event Duality (SED) property of IMTS, i.e., long stre... | [
"Ziyu Zhou",
"Yuchen Fang",
"Weilin Ruan",
"Shiyu Wang",
"James Kwok",
"Yuxuan Liang"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2026-02-02T00:00:00 | https://arxiv.org/abs/2602.02230 | https://arxiv.org/pdf/2602.02230v2 | 2602.02230 | 10.48550/arXiv.2602.02230 | 0 | 0 | false | null | arXiv.org | 0.4618 |
0f8d0c64456ade09e8b431c207c62401c24df950d886db75485cb262cefa66c8 | [
"arxiv",
"semantic_scholar"
] | Seg-MoE: Multi-Resolution Segment-wise Mixture-of-Experts for Time Series Forecasting Transformers | Transformer-based models have recently made significant advances in accurate time-series forecasting, but even these architectures struggle to scale efficiently while capturing long-term temporal dynamics. Mixture-of-Experts (MoE) layers are a proven solution to scaling problems in natural language processing. However,... | [
"Evandro S. Ortigossa",
"Eran Segal"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2026-01-29T00:00:00 | https://arxiv.org/abs/2601.21641 | https://arxiv.org/pdf/2601.21641v2 | 2601.21641 | 10.48550/arXiv.2601.21641 | 2 | 0 | false | null | arXiv.org | 0.4572 |
a195cffd2595d091b07b596ec82840e106441496ffe1e05e8a00dc8e6c78242e | [
"arxiv",
"semantic_scholar"
] | TimeCatcher: A Variational Framework for Volatility-Aware Forecasting of Non-Stationary Time Series | Recent lightweight MLP-based models have achieved strong performance in time series forecasting by capturing stable trends and seasonal patterns. However, their effectiveness hinges on an implicit assumption of local stationarity assumption, making them prone to errors in long-term forecasting of highly non-stationary ... | [
"Zhiyu Chen",
"Minhao Liu",
"Yanru Zhang"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2026-01-28T00:00:00 | https://arxiv.org/abs/2601.20448 | https://arxiv.org/pdf/2601.20448v1 | 2601.20448 | 10.48550/arXiv.2601.20448 | 0 | 0 | true | https://github.com/ColaPrinceCHEN/TimeCatcher | arXiv.org | 0.7048 |
4e0967db5e44b07db6bf9d4f84c322a4b2e3cc68b982e9ba922783899b7f7267 | [
"arxiv",
"semantic_scholar"
] | PatchFormer: A Patch-Based Time Series Foundation Model with Hierarchical Masked Reconstruction and Cross-Domain Transfer Learning for Zero-Shot Multi-Horizon Forecasting | Time series forecasting is a fundamental problem with applications in climate, energy, healthcare, and finance. Many existing approaches require domain-specific feature engineering and substantial labeled data for each task. We introduce PatchFormer, a patch-based time series foundation model that uses hierarchical mas... | [
"Olaf Yunus Laitinen Imanov",
"Derya Umut Kulali",
"Taner Yilmaz"
] | [
"cs.LG",
"eess.SP"
] | [
"Computer Science",
"Engineering"
] | 2026-01-28T00:00:00 | https://arxiv.org/abs/2601.20845 | https://arxiv.org/pdf/2601.20845v1 | 2601.20845 | 10.48550/arXiv.2601.20845 | 0 | 0 | false | null | arXiv.org | 0.456 |
4245936eb2f615a7fa1aaf99235e0a5783c9154617e8808bca7f098ea5201145 | [
"arxiv",
"semantic_scholar"
] | ScatterFusion: A Hierarchical Scattering Transform Framework for Enhanced Time Series Forecasting | Time series forecasting presents significant challenges due to the complex temporal dependencies at multiple time scales. This paper introduces ScatterFusion, a novel framework that synergistically integrates scattering transforms with hierarchical attention mechanisms for robust time series forecasting. Our approach c... | [
"Wei Li"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2026-01-28T00:00:00 | https://arxiv.org/abs/2601.20401 | https://arxiv.org/pdf/2601.20401v1 | 2601.20401 | 10.48550/arXiv.2601.20401 | 1 | 0 | false | null | IEEE International Conference on Acoustics, Speech, and Signal Processing | 0.456 |
121f985d57a8d66848605143e79d8a90c3d0d3d29b5a8cd83cfab07144a1b2d4 | [
"arxiv",
"semantic_scholar"
] | Intermittent time series forecasting: local vs global models | Forecasting intermittent time series, which contain zeros, is a crucial challenge in supply chains as inventory policies require probabilistic forecasts to establish safety levels. Intermittent time series are commonly forecast using local models, trained individually on each time series. In the last years global model... | [
"Stefano Damato",
"NicolΓ² Rubattu",
"Dario Azzimonti",
"Giorgio Corani"
] | [
"stat.ML",
"cs.LG"
] | [
"Mathematics",
"Computer Science"
] | 2026-01-20T00:00:00 | https://arxiv.org/abs/2601.14031 | https://arxiv.org/pdf/2601.14031v2 | 2601.14031 | 10.48550/arXiv.2601.14031 | 0 | 0 | false | null | arXiv.org | 0.4469 |
998bea0a41dd850b2327810d99662b63ec79b12db0cc016ecf29b3c41ec73e84 | [
"arxiv",
"semantic_scholar"
] | Distilling Time Series Foundation Models for Efficient Forecasting | Time Series foundation models (TSFMs) deliver strong forecasting performance through large-scale pretraining, but their large parameter sizes make deployment costly. While knowledge distillation offers a natural and effective approach for model compression, techniques developed for general machine learning tasks are no... | [
"Yuqi Li",
"Kuiye Ding",
"Chuanguang Yang",
"Szu-Yu Chen",
"Yingli Tian"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2026-01-19T00:00:00 | https://arxiv.org/abs/2601.12785 | https://arxiv.org/pdf/2601.12785v1 | 2601.12785 | 10.48550/arXiv.2601.12785 | 4 | 0 | true | https://github.com/itsnotacie/DistilTS-ICASSP2026 | IEEE International Conference on Acoustics, Speech, and Signal Processing | 0.6889 |
60b5ff7a3aedd3fec3979e4a8999ae53329758c331649447e7864244c66a3e39 | [
"arxiv",
"semantic_scholar"
] | Trend-Adjusted Time Series Models with an Application to Gold Price Forecasting | Time series data play a critical role in various fields, including finance, healthcare, marketing, and engineering. A wide range of techniques (from classical statistical models to neural network-based approaches such as Long Short-Term Memory (LSTM)) have been employed to address time series forecasting challenges. In... | [
"Sina Kazemdehbashi"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2026-01-19T00:00:00 | https://arxiv.org/abs/2601.12706 | https://arxiv.org/pdf/2601.12706v2 | 2601.12706 | 10.48550/arXiv.2601.12706 | 0 | 0 | false | null | arXiv.org | 0.4457 |
9e07c3f3ec17575e9bd9df8a0bb5cad5ad0199c9a96b8993c41c807c7e66b07c | [
"arxiv",
"semantic_scholar"
] | Patch-Level Tokenization with CNN Encoders and Attention for Improved Transformer Time-Series Forecasting | Transformer-based models have shown strong performance in time-series forecasting by leveraging self-attention to model long-range temporal dependencies. However, their effectiveness depends critically on the quality and structure of input representations derived from raw multivariate time-series data, particularly as ... | [
"Saurish Nagrath",
"Saroj Kumar Panigrahy"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2026-01-18T00:00:00 | https://arxiv.org/abs/2601.12467 | https://arxiv.org/pdf/2601.12467v3 | 2601.12467 | 10.48550/arXiv.2601.12467 | 0 | 0 | false | null | arXiv.org | 0.4446 |
6acac29a7149291f600b5c9a191c10971fc05507a6cc0f79b616906267991d38 | [
"arxiv",
"semantic_scholar"
] | Shapelets-Enriched Selective Forecasting using Time Series Foundation Models | Time series foundation models have recently gained a lot of attention due to their ability to model complex time series data encompassing different domains including traffic, energy, and weather. Although they exhibit strong average zero-shot performance on forecasting tasks, their predictions on certain critical regio... | [
"Shivani Tomar",
"Seshu Tirupathi",
"Elizabeth Daly",
"Ivana Dusparic"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2026-01-16T00:00:00 | https://arxiv.org/abs/2601.11821 | https://arxiv.org/pdf/2601.11821v1 | 2601.11821 | 10.48550/arXiv.2601.11821 | 0 | 0 | false | null | arXiv.org | 0.4423 |
53cea33f0362b098d80f9d42407c4edba16a32377dcfd0da6b931148d4f9c349 | [
"arxiv",
"semantic_scholar"
] | ProbFM: Probabilistic Time Series Foundation Model with Uncertainty Decomposition | Time Series Foundation Models (TSFMs) have emerged as a promising approach for zero-shot financial forecasting, demonstrating strong transferability and data efficiency gains. However, their adoption in financial applications is hindered by fundamental limitations in uncertainty quantification: current approaches eithe... | [
"Arundeep Chinta",
"Lucas Vinh Tran",
"Jay Katukuri"
] | [
"cs.LG",
"cs.AI",
"q-fin.RM",
"q-fin.TR"
] | [
"Computer Science",
"Economics"
] | 2026-01-15T00:00:00 | https://arxiv.org/abs/2601.10591 | https://arxiv.org/pdf/2601.10591v1 | 2601.10591 | 10.48550/arXiv.2601.10591 | 0 | 0 | false | null | arXiv.org | 0.4411 |
5a77e80b128258237466e59e7ce541e12123271559b3f531e994fa25e0717daa | [
"arxiv",
"semantic_scholar"
] | The Promise of Time-Series Foundation Models for Agricultural Forecasting: Evidence from Commodity Prices | Forecasting agricultural markets remains challenging due to nonlinear dynamics, structural breaks, and sparse data. A long-standing belief holds that simple time-series methods outperform more advanced alternatives. This paper provides the first systematic evidence that this belief no longer holds with modern time-seri... | [
"Le Wang",
"Boyuan Zhang"
] | [
"econ.EM",
"stat.AP"
] | [
"Economics",
"Mathematics"
] | 2026-01-10T00:00:00 | https://arxiv.org/abs/2601.06371 | https://arxiv.org/pdf/2601.06371v2 | 2601.06371 | null | 0 | 0 | false | null | null | 0.2771 |
d0f0c435336580cfa09bc3e038d235ed2a3c48baf616e51b2e21e18833030447 | [
"arxiv",
"semantic_scholar"
] | A Unified Shape-Aware Foundation Model for Time Series Classification | Foundation models pre-trained on large-scale source datasets are reshaping the traditional training paradigm for time series classification. However, existing time series foundation models primarily focus on forecasting tasks and often overlook classification-specific challenges, such as modeling interpretable shapelet... | [
"Zhen Liu",
"Yucheng Wang",
"Boyuan Li",
"Junhao Zheng",
"Emadeldeen Eldele",
"Min Wu",
"Qianli Ma"
] | [
"cs.LG",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2026-01-10T00:00:00 | https://arxiv.org/abs/2601.06429 | https://arxiv.org/pdf/2601.06429v1 | 2601.06429 | 10.48550/arXiv.2601.06429 | 2 | 0 | false | null | AAAI Conference on Artificial Intelligence | 0.4354 |
c4f61dc414f5f6126d5947ab11338f2549c13a202888c9d96501920c803d3292 | [
"arxiv",
"semantic_scholar"
] | Explainable time-series forecasting with sampling-free SHAP for Transformers | Time-series forecasts are essential for planning and decision-making in many domains. Explainability is key to building user trust and meeting transparency requirements. Shapley Additive Explanations (SHAP) is a popular explainable AI framework, but it lacks efficient implementations for time series and often assumes f... | [
"Matthias Hertel",
"Sebastian PΓΌtz",
"Ralf Mikut",
"Veit Hagenmeyer",
"Benjamin SchΓ€fer"
] | [
"cs.LG"
] | [
"Medicine",
"Computer Science"
] | 2025-12-23T00:00:00 | https://arxiv.org/abs/2512.20514 | https://arxiv.org/pdf/2512.20514v1 | 2512.20514 | 10.1038/s41467-026-73243-5 | 1 | 0 | false | null | Nature Communications | 0.4148 |
7b9a965f0a9d839558336958ad69064df99f49ce27b0e97a76030e55b75536a6 | [
"arxiv",
"semantic_scholar"
] | Conversational Time Series Foundation Models: Towards Explainable and Effective Forecasting | The proliferation of time series foundation models has created a landscape where no single method achieves consistent superiority, framing the central challenge not as finding the best model, but as orchestrating an optimal ensemble with interpretability. While Large Language Models (LLMs) offer powerful reasoning capa... | [
"Defu Cao",
"Michael Gee",
"Jinbo Liu",
"Hengxuan Wang",
"Wei Yang",
"Rui Wang",
"Yan Liu"
] | [
"cs.AI"
] | [
"Computer Science"
] | 2025-12-17T00:00:00 | https://arxiv.org/abs/2512.16022 | https://arxiv.org/pdf/2512.16022v1 | 2512.16022 | 10.48550/arXiv.2512.16022 | 7 | 1 | false | null | arXiv.org | 0.4079 |
7a4eab820bac7c2c90e33a157628a1c87fef0b3268e376813c159b19f8861e02 | [
"arxiv",
"semantic_scholar"
] | Adaptive Information Routing for Multimodal Time Series Forecasting | Time series forecasting is a critical task for artificial intelligence with numerous real-world applications. Traditional approaches primarily rely on historical time series data to predict the future values. However, in practical scenarios, this is often insufficient for accurate predictions due to the limited informa... | [
"Jun Seo",
"Hyeokjun Choe",
"Seohui Bae",
"Soyeon Park",
"Wonbin Ahn",
"Taeyoon Lim",
"Junhyeok Kang",
"Sangjun Han",
"Jaehoon Lee",
"Dongwan Kang",
"Minjae Kim",
"Sungdong Yoo",
"Soonyoung Lee"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2025-12-11T00:00:00 | https://arxiv.org/abs/2512.10229 | https://arxiv.org/pdf/2512.10229v3 | 2512.10229 | 10.48550/arXiv.2512.10229 | 0 | 0 | false | null | arXiv.org | 0.401 |
453d93ea0bd94c0d8ceb75dc403f6f70f1d37a5dc4a0ab0e421452f89c5e2e8d | [
"arxiv",
"semantic_scholar"
] | Time Series Foundation Models for Process Model Forecasting | Process Model Forecasting (PMF) aims to predict how the control-flow structure of a process evolves over time by modeling the temporal dynamics of directly-follows (DF) relations, complementing predictive process monitoring that focuses on single-case prefixes. Prior benchmarks show that machine learning and deep learn... | [
"Yongbo Yu",
"Jari Peeperkorn",
"Johannes De Smedt",
"Jochen De Weerdt"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2025-12-08T00:00:00 | https://arxiv.org/abs/2512.07624 | https://arxiv.org/pdf/2512.07624v1 | 2512.07624 | 10.48550/arXiv.2512.07624 | 0 | 0 | false | null | arXiv.org | 0.3976 |
d1a21263adea07c9f7347c770db584856f3901611d8ef6ec92f102c747e5da04 | [
"arxiv",
"semantic_scholar"
] | In-Context and Few-Shots Learning for Forecasting Time Series Data based on Large Language Models | Existing data-driven approaches in modeling and predicting time series data include ARIMA (Autoregressive Integrated Moving Average), Transformer-based models, LSTM (Long Short-Term Memory) and TCN (Temporal Convolutional Network). These approaches, and in particular deep learning-based models such as LSTM and TCN, hav... | [
"Saroj Gopali",
"Bipin Chhetri",
"Deepika Giri",
"Sima Siami-Namini",
"Akbar Siami Namin"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2025-12-08T00:00:00 | https://arxiv.org/abs/2512.07705 | https://arxiv.org/pdf/2512.07705v1 | 2512.07705 | 10.1109/BigData66926.2025.11401073 | 1 | 0 | false | null | BigData Congress [Services Society] | 0.3976 |
076eb10c9b44e8d3f571c13281929d29efecb57f25b088538eda6c796a8beae9 | [
"arxiv",
"semantic_scholar"
] | Robust Tabular Foundation Models | The development of tabular foundation models (TFMs) has accelerated in recent years, showing strong potential to outperform traditional ML methods for structured data. A key finding is that TFMs can be pretrained entirely on synthetic datasets, opening opportunities to design data generators that encourage desirable mo... | [
"Matthew Peroni",
"Franck Le",
"Vadim Sheinin"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2025-12-02T00:00:00 | https://arxiv.org/abs/2512.03307 | https://arxiv.org/pdf/2512.03307v1 | 2512.03307 | 10.48550/arXiv.2512.03307 | 1 | 0 | false | null | arXiv.org | 0.3907 |
fcd97c78b6d7efea5ff00c1072d6e83ba654fbcc102edeefa4ca5e1ea3af0440 | [
"arxiv",
"semantic_scholar"
] | CLEF: Clinically-Guided Contrastive Learning for Electrocardiogram Foundation Models | The electrocardiogram (ECG) is a key diagnostic tool in cardiovascular health. Single-lead ECG recording is integrated into both clinical-grade and consumer wearables. While self-supervised pretraining of foundation models on unlabeled ECGs improves diagnostic performance, existing approaches do not incorporate domain ... | [
"Yuxuan Shu",
"Peter H. Charlton",
"Fahim Kawsar",
"Jussi Hernesniemi",
"Mohammad Malekzadeh"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2025-12-01T00:00:00 | https://arxiv.org/abs/2512.02180 | https://arxiv.org/pdf/2512.02180v1 | 2512.02180 | 10.48550/arXiv.2512.02180 | 3 | 0 | true | https://github.com/Nokia-Bell-Labs/ecg-foundation-model | arXiv.org | 0.6021 |
0a61099ae12b410148d71c5c49dbb0ae2696cb22f1e8fd2ade673ece04ae701f | [
"arxiv",
"semantic_scholar"
] | Zero-Shot Transfer Capabilities of the Sundial Foundation Model for Leaf Area Index Forecasting | This work investigates the zero-shot forecasting capability of time series foundation models for Leaf Area Index (LAI) forecasting in agricultural monitoring. Using the HiQ dataset (U.S., 2000-2022), we systematically compare statistical baselines, a fully supervised LSTM, and the Sundial foundation model under multipl... | [
"Peining Zhang",
"Hongchen Qin",
"Haochen Zhang",
"Ziqi Guo",
"Guiling Wang",
"Jinbo Bi"
] | [
"cs.LG",
"cs.AI",
"cs.CV"
] | [
"Computer Science"
] | 2025-11-25T00:00:00 | https://arxiv.org/abs/2511.20004 | https://arxiv.org/pdf/2511.20004v2 | 2511.20004 | 10.48550/arXiv.2511.20004 | 0 | 0 | false | null | arXiv.org | 0.3827 |
20e99198add138e01bd93481d5a1a356f4542377f32e31ced758b9bb74e50c6a | [
"arxiv",
"semantic_scholar"
] | Tiny-TSM: Efficiently Training a Lightweight SOTA Time Series Foundation Model | We present Tiny-TSM, a time series foundation model characterized by small scale, economical training, and state-of-the-art performance. It comprises 23M total parameters, trained on a single A100 GPU in less than a week using a new synthetic data generation and data augmentation pipeline (SynthTS). Without any neural ... | [
"Felix Birkel"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2025-11-24T00:00:00 | https://arxiv.org/abs/2511.19272 | https://arxiv.org/pdf/2511.19272v1 | 2511.19272 | 10.48550/arXiv.2511.19272 | 0 | 0 | false | null | arXiv.org | 0.3816 |
2327d1e3416aebf7bf6550a5cbd73eff8ce2145014fb2b4363b13d21a7bccb81 | [
"arxiv",
"semantic_scholar"
] | TiCT: A Synthetically Pre-Trained Foundation Model for Time Series Classification | The ubiquity of time series data creates a strong demand for general-purpose foundation models, yet developing them for classification remains a significant challenge, largely due to the high cost of labeled data. Foundation models capable of in-context learning (ICL) offer a powerful solution, adapting to new tasks wi... | [
"Chin-Chia Michael Yeh",
"Uday Singh Saini",
"Junpeng Wang",
"Xin Dai",
"Xiran Fan",
"Jiarui Sun",
"Yujie Fan",
"Yan Zheng"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2025-11-24T00:00:00 | https://arxiv.org/abs/2511.19694 | https://arxiv.org/pdf/2511.19694v2 | 2511.19694 | 10.48550/arXiv.2511.19694 | 1 | 0 | false | null | arXiv.org | 0.3816 |
c5dbfa612630a75a6297579a45583ffcdf98558fe0dc3814b15531ebf1ce181a | [
"arxiv",
"semantic_scholar"
] | KAN vs LSTM Performance in Time Series Forecasting | This paper compares Kolmogorov-Arnold Networks (KAN) and Long Short-Term Memory networks (LSTM) for forecasting non-deterministic stock price data, evaluating predictive accuracy versus interpretability trade-offs using Root Mean Square Error (RMSE).LSTM demonstrates substantial superiority across all tested prediction... | [
"Tabish Ali Rather",
"S M Mahmudul Hasan Joy",
"Nadezda Sukhorukova",
"Federico Frascoli"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2025-11-23T00:00:00 | https://arxiv.org/abs/2511.18613 | https://arxiv.org/pdf/2511.18613v1 | 2511.18613 | 10.48550/arXiv.2511.18613 | 1 | 0 | false | null | arXiv.org | 0.3804 |
0669f1c82d8e4cf5424ef9f2ab9e08268c702e0692afc844fa2d2e12c07c7380 | [
"arxiv",
"semantic_scholar"
] | Accelerating Time Series Foundation Models with Speculative Decoding | Modern web applications--from real-time content recommendation and dynamic pricing to CDN optimization--increasingly rely on time-series forecasting to deliver personalized experiences to billions of users. Large-scale Transformer-based models have achieved state-of-the-art performance in time-series forecasting but su... | [
"Pranav Subbaraman",
"Fang Sun",
"Yue Yao",
"Huacong Tang",
"Xiao Luo",
"Yizhou Sun"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2025-11-22T00:00:00 | https://arxiv.org/abs/2511.18191 | https://arxiv.org/pdf/2511.18191v1 | 2511.18191 | 10.48550/arXiv.2511.18191 | 1 | 1 | true | https://github.com/PranavSubbaraman/STRIDE | arXiv.org | 0.5861 |
0f323f242f8e3020cd0f4391a666e9bcc730c1c0c40b36118d19af54e304f4a8 | [
"arxiv",
"semantic_scholar"
] | Optimal Look-back Horizon for Time Series Forecasting in Federated Learning | Selecting an appropriate look-back horizon remains a fundamental challenge in time series forecasting (TSF), particularly in the federated learning scenarios where data is decentralized, heterogeneous, and often non-independent. While recent work has explored horizon selection by preserving forecasting-relevant informa... | [
"Dahao Tang",
"Nan Yang",
"Yanli Li",
"Zhiyu Zhu",
"Zhibo Jin",
"Dong Yuan"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2025-11-16T00:00:00 | https://arxiv.org/abs/2511.12791 | https://arxiv.org/pdf/2511.12791v3 | 2511.12791 | 10.48550/arXiv.2511.12791 | 2 | 0 | false | null | AAAI Conference on Artificial Intelligence | 0.3724 |
07bdf3b969bc00f0581c19bda1480e8fce2b4902ac9729aa192dc901b6dd7134 | [
"arxiv",
"semantic_scholar"
] | EMAformer: Enhancing Transformer through Embedding Armor for Time Series Forecasting | Multivariate time series forecasting is crucial across a wide range of domains. While presenting notable progress for the Transformer architecture, iTransformer still lags behind the latest MLP-based models. We attribute this performance gap to unstable inter-channel relationships. To bridge this gap, we propose EMAfor... | [
"Zhiwei Zhang",
"Xinyi Du",
"Xuanchi Guo",
"Weihao Wang",
"Wenjuan Han"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2025-11-11T00:00:00 | https://arxiv.org/abs/2511.08396 | https://arxiv.org/pdf/2511.08396v1 | 2511.08396 | 10.48550/arXiv.2511.08396 | 2 | 0 | true | https://github.com/PlanckChang/EMAformer | AAAI Conference on Artificial Intelligence | 0.5667 |
8e0c141037c289ce220bed64a9fbee72a3523cd5d23dd917a4115b9a8732724b | [
"arxiv",
"semantic_scholar"
] | Bitcoin Forecasting with Classical Time Series Models on Prices and Volatility | This paper evaluates the performance of classical time series models in forecasting Bitcoin prices, focusing on ARIMA, SARIMA, GARCH, and EGARCH. Daily price data from 2010 to 2020 were analyzed, with models trained on the first 90 percent and tested on the final 10 percent. Forecast accuracy was assessed using MAE, RM... | [
"Anmar Kareem",
"Alexander Aue"
] | [
"q-fin.ST"
] | [
"Economics"
] | 2025-11-09T00:00:00 | https://arxiv.org/abs/2511.06224 | https://arxiv.org/pdf/2511.06224v1 | 2511.06224 | null | 1 | 1 | false | null | null | 0.2319 |
192780847161989db5bb5103ad967a90a21d26f5f040561a13cb14bedfa9ed86 | [
"arxiv",
"semantic_scholar"
] | Frequency Matters: When Time Series Foundation Models Fail Under Spectral Shift | Time series foundation models (TSFMs) have shown strong results on public benchmarks, prompting comparisons to a "BERT moment" for time series. Their effectiveness in industrial settings, however, remains uncertain. We examine why TSFMs often struggle to generalize and highlight spectral shift (a mismatch between the d... | [
"Tianze Wang",
"Sofiane Ennadir",
"John Pertoft",
"Gabriela Zarzar Gandler",
"Lele Cao",
"Zineb Senane",
"Styliani Katsarou",
"Sahar Asadi",
"Axel Karlsson",
"Oleg Smirnov"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2025-11-06T00:00:00 | https://arxiv.org/abs/2511.05619 | https://arxiv.org/pdf/2511.05619v1 | 2511.05619 | 10.48550/arXiv.2511.05619 | 1 | 0 | false | null | arXiv.org | 0.3609 |
d4d353dfa58d5b07a5a08cac58ef292a84bf5dc448feaed89b014a8d4bbbba78 | [
"arxiv",
"semantic_scholar"
] | Leveraging Generic Time Series Foundation Models for EEG Classification | Foundation models for time series are emerging as powerful general-purpose backbones, yet their potential for domain-specific biomedical signals such as electroencephalography (EEG) remains rather unexplored. In this work, we investigate the applicability a recently proposed time series classification foundation model,... | [
"ThΓ©o Gnassounou",
"Yessin Moakher",
"Shifeng Xie",
"Vasilii Feofanov",
"Ievgen Redko"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2025-10-31T00:00:00 | https://arxiv.org/abs/2510.27522 | https://arxiv.org/pdf/2510.27522v1 | 2510.27522 | 10.48550/arXiv.2510.27522 | 3 | 0 | false | null | arXiv.org | 0.3541 |
71b3860e50a965c7742515d0a2e0599281696e10c2ce967baa52f032bc975b92 | [
"arxiv",
"semantic_scholar"
] | Pre-trained Forecasting Models: Strong Zero-Shot Feature Extractors for Time Series Classification | Recent research on time series foundation models has primarily focused on forecasting, leaving it unclear how generalizable their learned representations are. In this study, we examine whether frozen pre-trained forecasting models can provide effective representations for classification. To this end, we compare differe... | [
"Andreas Auer",
"Daniel Klotz",
"Sebastinan BΓΆck",
"Sepp Hochreiter"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2025-10-30T00:00:00 | https://arxiv.org/abs/2510.26777 | https://arxiv.org/pdf/2510.26777v1 | 2510.26777 | 10.48550/arXiv.2510.26777 | 5 | 1 | false | null | arXiv.org | 0.3529 |
2664bf0c855da7ed33fc2b389962347a3166532ebb9576614d42d68b0d4bfd6a | [
"arxiv",
"semantic_scholar"
] | Time-Series Foundation Models for ISP Traffic Forecasting | Accurate network-traffic forecasting enables proactive capacity planning and anomaly detection in Internet Service Provider (ISP) networks. Recent advances in time-series foundation models (TSFMs) have demonstrated strong zero-shot and few-shot generalization across diverse domains, yet their effectiveness for computer... | [
"Fan Liu",
"Behrooz Farkiani",
"Patrick Crowley"
] | [
"cs.NI"
] | [
"Computer Science"
] | 2025-10-29T00:00:00 | https://arxiv.org/abs/2511.17529 | https://arxiv.org/pdf/2511.17529v2 | 2511.17529 | 10.48550/arXiv.2511.17529 | 0 | 0 | false | null | arXiv.org | 0.3518 |
c566fc0c41ac0c1bdd7f0f21332a111f6299efcaeef14080911aedf1d5d6c3de | [
"arxiv",
"semantic_scholar"
] | Solar flare forecasting with foundational transformer models across image, video, and time-series modalities | We present a comparative study of transformer-based architectures for solar flare forecasting using heterogeneous data modalities, including images, video sequences, and time-series observations. Our analysis evaluates three recent foundational models - SigLIP2 for image encoding, VideoMAE for spatio-temporal video rep... | [
"S. Riggi",
"P. Romano",
"A. Pilzer",
"U. Becciani"
] | [
"astro-ph.IM",
"astro-ph.SR"
] | [
"Physics",
"Computer Science"
] | 2025-10-27T00:00:00 | https://arxiv.org/abs/2510.23400 | https://arxiv.org/pdf/2510.23400v2 | 2510.23400 | 10.1016/j.ascom.2025.101042 | 1 | 0 | false | null | Astronomy and Computing | 0.3495 |
f1f6df6a123f4edfa2256e9b7917fde47d71b78ae08439226b2839654e6dff91 | [
"arxiv",
"semantic_scholar"
] | SEMPO: Lightweight Foundation Models for Time Series Forecasting | The recent boom of large pre-trained models witnesses remarkable success in developing foundation models (FMs) for time series forecasting. Despite impressive performance across diverse downstream forecasting tasks, existing time series FMs possess massive network architectures and require substantial pre-training on l... | [
"Hui He",
"Kun Yi",
"Yuanchi Ma",
"Qi Zhang",
"Zhendong Niu",
"Guansong Pang"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2025-10-22T00:00:00 | https://arxiv.org/abs/2510.19710 | https://arxiv.org/pdf/2510.19710v1 | 2510.19710 | 10.48550/arXiv.2510.19710 | 3 | 1 | true | https://github.com/mala-lab/SEMPO | arXiv.org | 0.5312 |
4fd2b2f51e1cb8661fe0af5b98f39a650bca9a4d88839ec58b4e461dac3513f5 | [
"arxiv",
"semantic_scholar"
] | QKCV Attention: Enhancing Time Series Forecasting with Static Categorical Embeddings for Both Lightweight and Pre-trained Foundation Models | In real-world time series forecasting tasks, category information plays a pivotal role in capturing inherent data patterns. This paper introduces QKCV (Query-Key-Category-Value) attention, an extension of the traditional QKV framework that incorporates a static categorical embedding C to emphasize category-specific inf... | [
"Hao Wang",
"Baojun Ma"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2025-10-21T00:00:00 | https://arxiv.org/abs/2510.20222 | https://arxiv.org/pdf/2510.20222v1 | 2510.20222 | 10.48550/arXiv.2510.20222 | 0 | 0 | false | null | arXiv.org | 0.3426 |
c0d588e03f4c002eacbc24a8a7df8fc700a128e46ebab3ac85bb5e37fed6a669 | [
"arxiv",
"semantic_scholar"
] | Benchmarking Probabilistic Time Series Forecasting Models on Neural Activity | Neural activity forecasting is central to understanding neural systems and enabling closed-loop control. While deep learning has recently advanced the state-of-the-art in the time series forecasting literature, its application to neural activity forecasting remains limited. To bridge this gap, we systematically evaluat... | [
"Ziyu Lu",
"Anna J. Li",
"Alexander E. Ladd",
"Pascha Matveev",
"Aditya Deole",
"Eric Shea-Brown",
"J. Nathan Kutz",
"Nicholas A. Steinmetz"
] | [
"cs.LG",
"q-bio.NC",
"stat.ML"
] | [
"Computer Science",
"Biology",
"Mathematics",
"Medicine"
] | 2025-10-20T00:00:00 | https://arxiv.org/abs/2510.18037 | https://arxiv.org/pdf/2510.18037v2 | 2510.18037 | 10.48550/arXiv.2510.18037 | 1 | 0 | false | null | arXiv.org | 0.3415 |
34b5b32eb5f705be6a2355cdab227bdd8c3d283da147f08e57e1dbede12943b1 | [
"arxiv",
"semantic_scholar"
] | Beyond Accuracy: Are Time Series Foundation Models Well-Calibrated? | The recent development of foundation models for time series data has generated considerable interest in using such models across a variety of applications. Although foundation models achieve state-of-the-art predictive performance, their calibration properties remain relatively underexplored, despite the fact that cali... | [
"Coen Adler",
"Yuxin Chang",
"Felix Draxler",
"Samar Abdi",
"Padhraic Smyth"
] | [
"cs.LG",
"cs.AI",
"stat.ME",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2025-10-17T00:00:00 | https://arxiv.org/abs/2510.16060 | https://arxiv.org/pdf/2510.16060v2 | 2510.16060 | 10.48550/arXiv.2510.16060 | 0 | 0 | false | null | arXiv.org | 0.338 |
54b8666124ce554d714789b5896ae66dae9f27964084eeabe16b59d1d284e189 | [
"arxiv",
"semantic_scholar"
] | CoRA: Covariate-Aware Adaptation of Time Series Foundation Models | Time Series Foundation Models (TSFMs) have shown significant impact through their model capacity, scalability, and zero-shot generalization. However, due to the heterogeneity of inter-variate dependencies and the backbone scalability on large-scale multivariate datasets, most TSFMs are typically pre-trained on univaria... | [
"Guo Qin",
"Zhi Chen",
"Yong Liu",
"Zhiyuan Shi",
"Haixuan Liu",
"Xiangdong Huang",
"Jianmin Wang",
"Mingsheng Long"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2025-10-14T00:00:00 | https://arxiv.org/abs/2510.12681 | https://arxiv.org/pdf/2510.12681v1 | 2510.12681 | 10.48550/arXiv.2510.12681 | 1 | 0 | false | null | arXiv.org | 0.3346 |
02ba6795e9f1cea20f675d452c8b5e46a5812967faa6ba2606bfd3bdfa1d7f4b | [
"arxiv",
"semantic_scholar"
] | Why Do Transformers Fail to Forecast Time Series In-Context? | Time series forecasting (TSF) remains a challenging and largely unsolved problem in machine learning, despite significant recent efforts leveraging Large Language Models (LLMs), which predominantly rely on Transformer architectures. Empirical evidence consistently shows that even powerful Transformers often fail to out... | [
"Yufa Zhou",
"Yixiao Wang",
"Surbhi Goel",
"Anru R. Zhang"
] | [
"cs.LG",
"cs.AI",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2025-10-10T00:00:00 | https://arxiv.org/abs/2510.09776 | https://arxiv.org/pdf/2510.09776v1 | 2510.09776 | 10.48550/arXiv.2510.09776 | 6 | 1 | true | https://github.com/MasterZhou1/ICL-Time-Series | arXiv.org | 0.51 |
da09caa62d16bed8af67efcf97799cd733a9003c96948f2b7e9e1ede882f7fdc | [
"arxiv",
"semantic_scholar"
] | Synthetic Series-Symbol Data Generation for Time Series Foundation Models | Foundation models for time series analysis (TSA) have attracted significant attention. However, challenges such as training data scarcity and imbalance continue to hinder their development. Inspired by complex dynamic system theories, we design a series-symbol data generation mechanism, enabling the unrestricted creati... | [
"Wenxuan Wang",
"Kai Wu",
"Yujian Betterest Li",
"Dan Wang",
"Xiaoyu Zhang"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2025-10-09T00:00:00 | https://arxiv.org/abs/2510.08445 | https://arxiv.org/pdf/2510.08445v3 | 2510.08445 | 10.48550/arXiv.2510.08445 | 0 | 0 | true | https://github.com/wwhenxuan/SymTime | arXiv.org | 0.5082 |
09a960cea14f1a7ab2b5705096ea71fa6ae2a2505c4878d42db5bd1b3d2b8d4a | [
"arxiv",
"semantic_scholar"
] | HTMformer: Hybrid Time and Multivariate Transformer for Time Series Forecasting | Transformer-based methods have achieved impressive results in time series forecasting. However, existing Transformers still exhibit limitations in sequence modeling as they tend to overemphasize temporal dependencies. This incurs additional computational overhead without yielding corresponding performance gains. We fin... | [
"Tan Wang",
"Yun Wei Dong",
"Qi Wang"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2025-10-08T00:00:00 | https://arxiv.org/abs/2510.07084 | https://arxiv.org/pdf/2510.07084v3 | 2510.07084 | 10.48550/arXiv.2510.07084 | 1 | 0 | false | null | arXiv.org | 0.3277 |
b3622480fabb731be475b5898bc4b8ecb6d0a10cd036a3d5a10666b2941da4ce | [
"arxiv",
"semantic_scholar"
] | TimeFormer: Transformer with Attention Modulation Empowered by Temporal Characteristics for Time Series Forecasting | Although Transformers excel in natural language processing, their extension to time series forecasting remains challenging due to insufficient consideration of the differences between textual and temporal modalities. In this paper, we develop a novel Transformer architecture designed for time series data, aiming to max... | [
"Zhipeng Liu",
"Peibo Duan",
"Xuan Tang",
"Baixin Li",
"Yongsheng Huang",
"Mingyang Geng",
"Changsheng Zhang",
"Bin Zhang",
"Binwu Wang"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2025-10-08T00:00:00 | https://arxiv.org/abs/2510.06680 | https://arxiv.org/pdf/2510.06680v1 | 2510.06680 | 10.48550/arXiv.2510.06680 | 6 | 0 | false | null | Expert systems with applications | 0.3277 |
End of preview. Expand in Data Studio
Time Series Foundation Models Papers β FineSet
A research-paper dataset on Time Series Foundation Models Papers, assembled, deduplicated, and quality-scored by FineSet from arXiv and Semantic Scholar.
πΈ This is a dated snapshot β generated 2026-06-19. It is not auto-updated. Research on Time Series Foundation Models Papers moves fast β new papers land on arXiv every week. Want this same dataset refreshed daily, on a topic you choose? See the bottom. β
Why this dataset
- Quality-scored:
quality_scorefloat (0β1), blends citations with recency + code/venue signals β filter out the noise - Papers with code: 98 flagged via
has_codeβ find reproducible work fast - Deduplicated: arXiv + Semantic Scholar cross-referenced, duplicate records merged
- Clean JSONL: 439 records, one per line, normalized fields β no encoding garbage
Dataset details
- Records: 439
- Date range: 2021β2026
- Snapshot date: 2026-06-19 (frozen β see note above)
- Sources: arXiv, Semantic Scholar (cross-referenced, duplicates merged)
- arXiv categories: cs.LG
- Quality scoring: citations + recency + code/venue blend, 0β1 (p50=0.32, p90=0.543)
- Format: JSONL, one record per line
Fields
| Field | Type | Description |
|---|---|---|
| id | string | Deterministic SHA256 record id |
| sources | list | Which sources contributed (arxiv, semantic_scholar) |
| title | string | Paper title |
| abstract | string | Full abstract |
| authors | list | Author names |
| categories | list | arXiv category codes |
| fields_of_study | list | Semantic Scholar field tags |
| published_date | string | ISO 8601 date |
| url | string | arXiv abstract URL |
| pdf_url | string|null | Open-access PDF if available |
| arxiv_id | string|null | arXiv identifier |
| doi | string|null | DOI if available |
| citation_count | int | Citation count (Semantic Scholar) |
| influential_citation_count | int | Influential citations (Semantic Scholar) |
| has_code | bool | Code repo detected in the arXiv comment |
| code_url | string|null | GitHub URL if detected |
| venue | string|null | Publication venue |
| quality_score | float | 0β1, blended (citations + recency + code/venue) |
Quality score methodology
quality_score = max(impact, freshness), clamped to [0, 1], where:
- impact =
max( log10(citations+1)/4 , log10(influential_citations+1)/2 )β realized impact (0.5 at 100 citations, ~0.75 at 1,000, 1.0 at 10,000+). - freshness =
recency Γ (0.35 + 0.30Β·has_code + 0.20Β·has_venue)β a baseline for recent papers (so a strong paper published this week isn't scored 0 just for lacking citations), whererecencyis 1.0 for papers β€60 days old and decays linearly to 0 by ~18 months.
Old highly-cited papers score on impact; brand-new papers score on freshness; old uncited papers score ~0. Useful for filtering training data by quality, not just age.
π Want this on YOUR topic, updated daily?
This snapshot is frozen at 2026-06-19. The live FineSet pipeline keeps a dataset like this refreshed every day on whatever topic you describe β new papers in, dedup and quality scoring automatic, export as JSONL/Parquet or push straight to the Hub.
Tell me the topic you'd want and I'll run the pipeline on it β open a discussion on this dataset, it's free and it's how I decide what to build next.
β fineset.io β describe what you want to train on, get a dataset. Early-access waitlist open (referral skip available).
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