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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
7f15039e3892bd5846c428664ae5ccf73dee571b962bab39c97f8d456523636c | [
"arxiv",
"semantic_scholar"
] | Time series aggregation for optimization: One-size-fits-all? | One of the fundamental problems of using optimization models that use different time series as data input, is the trade-off between model accuracy and computational tractability. To overcome the computational intractability of these full optimization models, the dimension of input data and model size is commonly reduce... | [
"Sonja Wogrin"
] | [
"math.OC"
] | [
"Computer Science",
"Mathematics"
] | 2022-06-07T00:00:00 | https://arxiv.org/abs/2206.03186 | https://arxiv.org/pdf/2206.03186v1 | 2206.03186 | 10.1109/TSG.2023.3242467 | 19 | 4 | false | null | IEEE Transactions on Smart Grid | 0.3495 |
7eef1ea60ae236fcf6baeb8cee34263e902b271b4f90a7ef270fc569c3ba4b75 | [
"arxiv",
"semantic_scholar"
] | Non-stationary Transformers: Exploring the Stationarity in Time Series Forecasting | Transformers have shown great power in time series forecasting due to their global-range modeling ability. However, their performance can degenerate terribly on non-stationary real-world data in which the joint distribution changes over time. Previous studies primarily adopt stationarization to attenuate the non-statio... | [
"Yong Liu",
"Haixu Wu",
"Jianmin Wang",
"Mingsheng Long"
] | [
"cs.LG",
"eess.SP"
] | [
"Computer Science",
"Engineering"
] | 2022-05-28T00:00:00 | https://arxiv.org/abs/2205.14415 | https://arxiv.org/pdf/2205.14415v4 | 2205.14415 | 10.52202/068431-0718 | 882 | 78 | true | https://github.com/thuml/Nonstationary_Transformers | Neural Information Processing Systems | 0.9488 |
4143f5b84b251c7011f4c95164a2c0702a8acb024b120c2db5003394af7b5029 | [
"arxiv",
"semantic_scholar"
] | Are Transformers Effective for Time Series Forecasting? | Recently, there has been a surge of Transformer-based solutions for the long-term time series forecasting (LTSF) task. Despite the growing performance over the past few years, we question the validity of this line of research in this work. Specifically, Transformers is arguably the most successful solution to extract t... | [
"Ailing Zeng",
"Muxi Chen",
"Lei Zhang",
"Qiang Xu"
] | [
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2022-05-26T00:00:00 | https://arxiv.org/abs/2205.13504 | https://arxiv.org/pdf/2205.13504v3 | 2205.13504 | 10.48550/arXiv.2205.13504 | 3,906 | 433 | true | https://github.com/cure-lab/LTSF-Linear | AAAI Conference on Artificial Intelligence | 1 |
baa2464cd55fb43a4dfed3800c5d83157a055d04c30dd2b952fd26e9acfea15f | [
"arxiv",
"semantic_scholar"
] | Wind energy forecasting with missing values within a fully conditional specification framework | Wind power forecasting is essential to power system operation and electricity markets. As abundant data became available thanks to the deployment of measurement infrastructures and the democratization of meteorological modelling, extensive data-driven approaches have been developed within both point and probabilistic f... | [
"Honglin Wen",
"Pierre Pinson",
"Jie Gu",
"Zhijian Jin"
] | [
"stat.AP",
"eess.SY"
] | [
"Mathematics",
"Computer Science",
"Engineering"
] | 2022-03-15T00:00:00 | https://arxiv.org/abs/2203.08252 | https://arxiv.org/pdf/2203.08252v2 | 2203.08252 | 10.1016/j.ijforecast.2022.12.006 | 16 | 0 | false | null | International Journal of Forecasting | 0.3076 |
67f2326af9e454d376caea9815dea19831f94902344204af948c804ec0874a46 | [
"arxiv",
"semantic_scholar"
] | Robust Probabilistic Time Series Forecasting | Probabilistic time series forecasting has played critical role in decision-making processes due to its capability to quantify uncertainties. Deep forecasting models, however, could be prone to input perturbations, and the notion of such perturbations, together with that of robustness, has not even been completely estab... | [
"TaeHo Yoon",
"Youngsuk Park",
"Ernest K. Ryu",
"Yuyang Wang"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2022-02-24T00:00:00 | https://arxiv.org/abs/2202.11910 | https://arxiv.org/pdf/2202.11910v1 | 2202.11910 | null | 32 | 0 | false | null | International Conference on Artificial Intelligence and Statistics | 0.3796 |
1546b3bd20732f45c66ec323981307e7b8f59b22a04d747ca03c3843181d4e53 | [
"arxiv",
"semantic_scholar"
] | A Differential Attention Fusion Model Based on Transformer for Time Series Forecasting | Time series forecasting is widely used in the fields of equipment life cycle forecasting, weather forecasting, traffic flow forecasting, and other fields. Recently, some scholars have tried to apply Transformer to time series forecasting because of its powerful parallel training ability. However, the existing Transform... | [
"Benhan Li",
"Shengdong Du",
"Tianrui Li"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2022-02-23T00:00:00 | https://arxiv.org/abs/2202.11402 | https://arxiv.org/pdf/2202.11402v1 | 2202.11402 | null | 2 | 1 | false | null | arXiv.org | 0.1505 |
6d046e6fa6f36939240ceac1cdd7a62fa3bec93556280cc35a5603cd722734f3 | [
"arxiv",
"semantic_scholar"
] | Preformer: Predictive Transformer with Multi-Scale Segment-wise Correlations for Long-Term Time Series Forecasting | Transformer-based methods have shown great potential in long-term time series forecasting. However, most of these methods adopt the standard point-wise self-attention mechanism, which not only becomes intractable for long-term forecasting since its complexity increases quadratically with the length of time series, but ... | [
"Dazhao Du",
"Bing Su",
"Zhewei Wei"
] | [
"cs.LG",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2022-02-23T00:00:00 | https://arxiv.org/abs/2202.11356 | https://arxiv.org/pdf/2202.11356v1 | 2202.11356 | 10.1109/ICASSP49357.2023.10096881 | 84 | 4 | false | null | IEEE International Conference on Acoustics, Speech, and Signal Processing | 0.4824 |
30907e7f748972340c6aabc664927dc85e023cbe4bc7ba8444bbbd12a6630245 | [
"arxiv",
"semantic_scholar"
] | Combating Distribution Shift for Accurate Time Series Forecasting via Hypernetworks | Time series forecasting has widespread applications in urban life ranging from air quality monitoring to traffic analysis. However, accurate time series forecasting is challenging because real-world time series suffer from the distribution shift problem, where their statistical properties change over time. Despite exte... | [
"Wenying Duan",
"Xiaoxi He",
"Lu Zhou",
"Lothar Thiele",
"Hong Rao"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2022-02-22T00:00:00 | https://arxiv.org/abs/2202.10808 | https://arxiv.org/pdf/2202.10808v2 | 2202.10808 | 10.1109/ICPADS56603.2022.00121 | 18 | 1 | false | null | International Conference on Parallel and Distributed Systems | 0.3197 |
b19ca04ec305d19faf498a9a79beb5c15b2c4777402be20b6348f6a08f1c87b8 | [
"arxiv",
"semantic_scholar"
] | Multi-Objective Model Selection for Time Series Forecasting | Research on time series forecasting has predominantly focused on developing methods that improve accuracy. However, other criteria such as training time or latency are critical in many real-world applications. We therefore address the question of how to choose an appropriate forecasting model for a given dataset among ... | [
"Oliver Borchert",
"David Salinas",
"Valentin Flunkert",
"Tim Januschowski",
"Stephan Günnemann"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2022-02-17T00:00:00 | https://arxiv.org/abs/2202.08485 | https://arxiv.org/pdf/2202.08485v1 | 2202.08485 | null | 10 | 0 | true | null | arXiv.org | 0.2603 |
2219911ca20986c8cd4e6f7c776d328742bc9bb7824071660c672e376882e1fa | [
"arxiv",
"semantic_scholar"
] | Transformers in Time Series: A Survey | Transformers have achieved superior performances in many tasks in natural language processing and computer vision, which also triggered great interest in the time series community. Among multiple advantages of Transformers, the ability to capture long-range dependencies and interactions is especially attractive for tim... | [
"Qingsong Wen",
"Tian Zhou",
"Chaoli Zhang",
"Weiqi Chen",
"Ziqing Ma",
"Junchi Yan",
"Liang Sun"
] | [
"cs.LG",
"cs.AI",
"eess.SP",
"stat.ML"
] | [
"Computer Science",
"Engineering",
"Mathematics"
] | 2022-02-15T00:00:00 | https://arxiv.org/abs/2202.07125 | https://arxiv.org/pdf/2202.07125v5 | 2202.07125 | 10.24963/ijcai.2023/759 | 1,487 | 52 | true | https://github.com/qingsongedu/time-series-transformers-review | International Joint Conference on Artificial Intelligence | 0.8621 |
504e812d964020fbc980240997f1ad356df869050f99dd1243e82bffb79168db | [
"arxiv",
"semantic_scholar"
] | ETSformer: Exponential Smoothing Transformers for Time-series Forecasting | Transformers have been actively studied for time-series forecasting in recent years. While often showing promising results in various scenarios, traditional Transformers are not designed to fully exploit the characteristics of time-series data and thus suffer some fundamental limitations, e.g., they generally lack of d... | [
"Gerald Woo",
"Chenghao Liu",
"Doyen Sahoo",
"Akshat Kumar",
"Steven Hoi"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2022-02-03T00:00:00 | https://arxiv.org/abs/2202.01381 | https://arxiv.org/pdf/2202.01381v2 | 2202.01381 | null | 307 | 30 | true | https://github.com/salesforce/ETSformer | arXiv.org | 0.7457 |
5c736395feba1a5fd2ecdb7dd51eebfec1da6e14925423eb9439e89d5524ed7d | [
"arxiv",
"semantic_scholar"
] | Review of automated time series forecasting pipelines | Time series forecasting is fundamental for various use cases in different domains such as energy systems and economics. Creating a forecasting model for a specific use case requires an iterative and complex design process. The typical design process includes the five sections (1) data pre-processing, (2) feature engine... | [
"Stefan Meisenbacher",
"Marian Turowski",
"Kaleb Phipps",
"Martin Rätz",
"Dirk Müller",
"Veit Hagenmeyer",
"Ralf Mikut"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2022-02-03T00:00:00 | https://arxiv.org/abs/2202.01712 | https://arxiv.org/pdf/2202.01712v1 | 2202.01712 | 10.1002/widm.1475 | 87 | 5 | false | null | WIREs Data Mining and Knowledge Discovery (2022) e1475 | 0.4861 |
d052a242cc46021a754c3985a567e7a6d63a63c5b9fb82de1691800695e7ebd0 | [
"arxiv",
"semantic_scholar"
] | Monte Carlo EM for Deep Time Series Anomaly Detection | Time series data are often corrupted by outliers or other kinds of anomalies. Identifying the anomalous points can be a goal on its own (anomaly detection), or a means to improving performance of other time series tasks (e.g. forecasting). Recent deep-learning-based approaches to anomaly detection and forecasting commo... | [
"François-Xavier Aubet",
"Daniel Zügner",
"Jan Gasthaus"
] | [
"cs.LG",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2021-12-29T00:00:00 | https://arxiv.org/abs/2112.14436 | https://arxiv.org/pdf/2112.14436v1 | 2112.14436 | null | 7 | 0 | false | null | arXiv.org | 0.2258 |
46aababb953d0a5422dd8cf84ecab7beaccf1e25c1dbb4587a8a74a8768a1a49 | [
"arxiv",
"semantic_scholar"
] | Parameter Efficient Deep Probabilistic Forecasting | Probabilistic time series forecasting is crucial in many application domains such as retail, ecommerce, finance, or biology. With the increasing availability of large volumes of data, a number of neural architectures have been proposed for this problem. In particular, Transformer-based methods achieve state-of-the-art ... | [
"Olivier Sprangers",
"Sebastian Schelter",
"Maarten de Rijke"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2021-12-06T00:00:00 | https://arxiv.org/abs/2112.02905 | https://arxiv.org/pdf/2112.02905v2 | 2112.02905 | 10.1016/j.ijforecast.2021.11.011 | 44 | 0 | false | null | International Journal of Forecasting | 0.4133 |
e5d29cf8bed93bcc34992b22f95771359effa8a45296e8ca8cc92c034483ed08 | [
"arxiv",
"semantic_scholar"
] | Learning dynamical systems from data: A simple cross-validation perspective, part III: Irregularly-Sampled Time Series | A simple and interpretable way to learn a dynamical system from data is to interpolate its vector-field with a kernel. In particular, this strategy is highly efficient (both in terms of accuracy and complexity) when the kernel is data-adapted using Kernel Flows (KF)\cite{Owhadi19} (which uses gradient-based optimizatio... | [
"Jonghyeon Lee",
"Edward De Brouwer",
"Boumediene Hamzi",
"Houman Owhadi"
] | [
"stat.ML",
"cs.LG",
"math.DS",
"stat.CO"
] | [
"Mathematics",
"Computer Science"
] | 2021-11-25T00:00:00 | https://arxiv.org/abs/2111.13037 | https://arxiv.org/pdf/2111.13037v2 | 2111.13037 | 10.1016/j.physd.2023.133853 | 25 | 0 | false | null | Physica D: Nonlinear Phenomena Volume 454 , 15 November 2023, 133853 | 0.3537 |
4c372c13a41adb302467ce40b973e6d4fd66e3b16f57a47ae6e325cbb20fcf28 | [
"arxiv",
"semantic_scholar"
] | LoMEF: A Framework to Produce Local Explanations for Global Model Time Series Forecasts | Global Forecasting Models (GFM) that are trained across a set of multiple time series have shown superior results in many forecasting competitions and real-world applications compared with univariate forecasting approaches. One aspect of the popularity of statistical forecasting models such as ETS and ARIMA is their re... | [
"Dilini Rajapaksha",
"Christoph Bergmeir",
"Rob J Hyndman"
] | [
"cs.LG",
"cs.AI",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2021-11-13T00:00:00 | https://arxiv.org/abs/2111.07001 | https://arxiv.org/pdf/2111.07001v1 | 2111.07001 | 10.1016/j.ijforecast.2022.06.006 | 15 | 0 | false | null | International Journal of Forecasting | 0.301 |
a1d0c1d51ac901bd6d05869bcfc1a7d52098a4e7273650e22b6aa8a84f2f2122 | [
"arxiv",
"semantic_scholar"
] | Probabilistic Hierarchical Forecasting with Deep Poisson Mixtures | Hierarchical forecasting problems arise when time series have a natural group structure, and predictions at multiple levels of aggregation and disaggregation across the groups are needed. In such problems, it is often desired to satisfy the aggregation constraints in a given hierarchy, referred to as hierarchical coher... | [
"Kin G. Olivares",
"O. Nganba Meetei",
"Ruijun Ma",
"Rohan Reddy",
"Mengfei Cao",
"Lee Dicker"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2021-10-25T00:00:00 | https://arxiv.org/abs/2110.13179 | https://arxiv.org/pdf/2110.13179v8 | 2110.13179 | 10.1016/j.ijforecast.2023.04.007 | 36 | 1 | false | null | International Journal of Forecasting | 0.3921 |
36b7711563033f931657c7c877083d1a6f4b60d8aafe922f2c7e48c414ec3614 | [
"arxiv",
"semantic_scholar"
] | DMS, AE, DAA: methods and applications of adaptive time series model selection, ensemble, and financial evaluation | We introduce three adaptive time series learning methods, called Dynamic Model Selection (DMS), Adaptive Ensemble (AE), and Dynamic Asset Allocation (DAA). The methods respectively handle model selection, ensembling, and contextual evaluation in financial time series. Empirically, we use the methods to forecast the ret... | [
"Parley Ruogu Yang",
"Ryan Lucas"
] | [
"stat.AP",
"econ.EM",
"q-fin.ST",
"stat.ML",
"stat.OT"
] | [
"Mathematics",
"Economics"
] | 2021-10-21T00:00:00 | https://arxiv.org/abs/2110.11156 | https://arxiv.org/pdf/2110.11156v3 | 2110.11156 | null | 0 | 0 | false | null | null | 0 |
cac2bc2b8d3bbab98f444715aa1247d1bf517f0d625476ba4eea574f5eaceafe | [
"arxiv",
"semantic_scholar"
] | Probabilistic Time Series Forecasts with Autoregressive Transformation Models | Probabilistic forecasting of time series is an important matter in many applications and research fields. In order to draw conclusions from a probabilistic forecast, we must ensure that the model class used to approximate the true forecasting distribution is expressive enough. Yet, characteristics of the model itself, ... | [
"David Rügamer",
"Philipp F. M. Baumann",
"Thomas Kneib",
"Torsten Hothorn"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2021-10-15T00:00:00 | https://arxiv.org/abs/2110.08248 | https://arxiv.org/pdf/2110.08248v3 | 2110.08248 | 10.1007/s11222-023-10212-8 | 14 | 1 | false | null | Statistics and computing | 0.294 |
d2fd751119db495fec11f1661b1351f351ac98cce5283dc3d623ca7b1a51cce0 | [
"arxiv",
"semantic_scholar"
] | Well Googled is Half Done: Multimodal Forecasting of New Fashion Product Sales with Image-based Google Trends | New fashion product sales forecasting is a challenging problem that involves many business dynamics and cannot be solved by classical forecasting approaches. In this paper, we investigate the effectiveness of systematically probing exogenous knowledge in the form of Google Trends time series and combining it with multi... | [
"Geri Skenderi",
"Christian Joppi",
"Matteo Denitto",
"Marco Cristani"
] | [
"cs.CV",
"cs.LG"
] | [
"Computer Science"
] | 2021-09-20T00:00:00 | https://arxiv.org/abs/2109.09824 | https://arxiv.org/pdf/2109.09824v6 | 2109.09824 | 10.1002/for.3104 | 40 | 8 | true | https://github.com/HumaticsLAB/GTM-Transformer | Journal of Forecasting | 0.4771 |
7a8595eb54601b5090e22a1852812319848d752b537ec0efab20140aa67dbb77 | [
"arxiv",
"semantic_scholar"
] | A Study of Joint Graph Inference and Forecasting | We study a recent class of models which uses graph neural networks (GNNs) to improve forecasting in multivariate time series. The core assumption behind these models is that there is a latent graph between the time series (nodes) that governs the evolution of the multivariate time series. By parameterizing a graph in a... | [
"Daniel Zügner",
"François-Xavier Aubet",
"Victor Garcia Satorras",
"Tim Januschowski",
"Stephan Günnemann",
"Jan Gasthaus"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2021-09-10T00:00:00 | https://arxiv.org/abs/2109.04979 | https://arxiv.org/pdf/2109.04979v1 | 2109.04979 | null | 12 | 0 | false | null | arXiv.org | 0.2785 |
d23043f4a8768bdba685f09702a8a1db88b4d20e98bbdf6cfc16225b34a20517 | [
"arxiv",
"semantic_scholar"
] | TCCT: Tightly-Coupled Convolutional Transformer on Time Series Forecasting | Time series forecasting is essential for a wide range of real-world applications. Recent studies have shown the superiority of Transformer in dealing with such problems, especially long sequence time series input(LSTI) and long sequence time series forecasting(LSTF) problems. To improve the efficiency and enhance the l... | [
"Li Shen",
"Yangzhu Wang"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2021-08-29T00:00:00 | https://arxiv.org/abs/2108.12784 | https://arxiv.org/pdf/2108.12784v2 | 2108.12784 | 10.1016/j.neucom.2022.01.039 | 123 | 8 | false | null | Neurocomputing | 0.5234 |
4b93102d1db908cd47c4ae9a678ef6a44cae8bcc6ca2b1d50eee4e0f1d211b4a | [
"arxiv",
"semantic_scholar"
] | Transformers predicting the future. Applying attention in next-frame and time series forecasting | Recurrent Neural Networks were, until recently, one of the best ways to capture the timely dependencies in sequences. However, with the introduction of the Transformer, it has been proven that an architecture with only attention-mechanisms without any RNN can improve on the results in various sequence processing tasks ... | [
"Radostin Cholakov",
"Todor Kolev"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2021-08-18T00:00:00 | https://arxiv.org/abs/2108.08224 | https://arxiv.org/pdf/2108.08224v1 | 2108.08224 | null | 24 | 0 | false | null | arXiv.org | 0.3495 |
2294105a11853b818bf889384b2defe26b08a64792b36f8f14ada9d873f53fdf | [
"arxiv",
"semantic_scholar"
] | Tsformer: Time series Transformer for tourism demand forecasting | AI-based methods have been widely applied to tourism demand forecasting. However, current AI-based methods are short of the ability to process long-term dependency, and most of them lack interpretability. The Transformer used initially for machine translation shows an incredible ability to long-term dependency processi... | [
"Siyuan Yi",
"Xing Chen",
"Chuanming Tang"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2021-07-22T00:00:00 | https://arxiv.org/abs/2107.10977 | https://arxiv.org/pdf/2107.10977v1 | 2107.10977 | null | 4 | 0 | false | null | arXiv.org | 0.1747 |
1c5ea74fae5b88a3641e612ba334206924e38d005a0ca79f89ca02ae0ac34916 | [
"arxiv",
"semantic_scholar"
] | Dynamic functional time-series forecasts of foreign exchange implied volatility surfaces | This paper presents static and dynamic versions of univariate, multivariate, and multilevel functional time-series methods to forecast implied volatility surfaces in foreign exchange markets. We find that dynamic functional principal component analysis generally improves out-of-sample forecast accuracy. More specifical... | [
"Han Lin Shang",
"Fearghal Kearney"
] | [
"q-fin.ST",
"stat.AP",
"stat.CO"
] | [
"Economics",
"Mathematics"
] | 2021-07-22T00:00:00 | https://arxiv.org/abs/2107.14026 | https://arxiv.org/pdf/2107.14026v1 | 2107.14026 | 10.1016/j.ijforecast.2021.07.011 | 23 | 3 | false | null | International Journal of Forecasting | 0.3451 |
601422d9a129048af6914f4c4a0f629b2db33398abb7247348bbeba746286d57 | [
"arxiv",
"semantic_scholar"
] | Topological Attention for Time Series Forecasting | The problem of (point) forecasting $ \textit{univariate} $ time series is considered. Most approaches, ranging from traditional statistical methods to recent learning-based techniques with neural networks, directly operate on raw time series observations. As an extension, we study whether $\textit{local topological pro... | [
"Sebastian Zeng",
"Florian Graf",
"Christoph Hofer",
"Roland Kwitt"
] | [
"cs.LG",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2021-07-19T00:00:00 | https://arxiv.org/abs/2107.09031 | https://arxiv.org/pdf/2107.09031v1 | 2107.09031 | null | 40 | 1 | false | null | Neural Information Processing Systems | 0.4032 |
a2648627852380ba511149030091f8c8f16e5b871be02e276c3a0cded133ec08 | [
"arxiv",
"semantic_scholar"
] | Probabilistic Time Series Forecasting with Implicit Quantile Networks | Here, we propose a general method for probabilistic time series forecasting. We combine an autoregressive recurrent neural network to model temporal dynamics with Implicit Quantile Networks to learn a large class of distributions over a time-series target. When compared to other probabilistic neural forecasting models ... | [
"Adèle Gouttes",
"Kashif Rasul",
"Mateusz Koren",
"Johannes Stephan",
"Tofigh Naghibi"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2021-07-08T00:00:00 | https://arxiv.org/abs/2107.03743 | https://arxiv.org/pdf/2107.03743v1 | 2107.03743 | null | 31 | 6 | false | null | arXiv.org | 0.4225 |
68f5d4603dc5fc84f663c353325d037678e8e78041b33e8c68b989fc7e90bf3d | [
"arxiv",
"semantic_scholar"
] | SCINet: Time Series Modeling and Forecasting with Sample Convolution and Interaction | One unique property of time series is that the temporal relations are largely preserved after downsampling into two sub-sequences. By taking advantage of this property, we propose a novel neural network architecture that conducts sample convolution and interaction for temporal modeling and forecasting, named SCINet. Sp... | [
"Minhao Liu",
"Ailing Zeng",
"Muxi Chen",
"Zhijian Xu",
"Qiuxia Lai",
"Lingna Ma",
"Qiang Xu"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2021-06-17T00:00:00 | https://arxiv.org/abs/2106.09305 | https://arxiv.org/pdf/2106.09305v3 | 2106.09305 | 10.52202/068431-0421 | 819 | 47 | true | https://github.com/cure-lab/SCINet | Neural Information Processing Systems | 0.8406 |
d4bb68e4ba77ce7a1ed6342ebbbdd29420a799d6e22f88c60e79cd4644b39615 | [
"arxiv",
"semantic_scholar"
] | Monash Time Series Forecasting Archive | Many businesses and industries nowadays rely on large quantities of time series data making time series forecasting an important research area. Global forecasting models that are trained across sets of time series have shown a huge potential in providing accurate forecasts compared with the traditional univariate forec... | [
"Rakshitha Godahewa",
"Christoph Bergmeir",
"Geoffrey I. Webb",
"Rob J. Hyndman",
"Pablo Montero-Manso"
] | [
"cs.LG",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2021-05-14T00:00:00 | https://arxiv.org/abs/2105.06643 | https://arxiv.org/pdf/2105.06643v1 | 2105.06643 | null | 267 | 36 | false | null | Neural Information Processing Systems Track on Datasets and Benchmarks (2021) - forthcoming | 0.7841 |
90c80c524ce35900f82e99763f0634d86154645839ad12e7910cef0c82256af6 | [
"arxiv",
"semantic_scholar"
] | Time Series Forecasting via Learning Convolutionally Low-Rank Models | Recently, Liu and Zhang studied the rather challenging problem of time series forecasting from the perspective of compressed sensing. They proposed a no-learning method, named Convolution Nuclear Norm Minimization (CNNM), and proved that CNNM can exactly recover the future part of a series from its observed part, provi... | [
"Guangcan Liu"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2021-04-23T00:00:00 | https://arxiv.org/abs/2104.11510 | https://arxiv.org/pdf/2104.11510v5 | 2104.11510 | 10.1109/TIT.2022.3144605 | 19 | 1 | false | null | IEEE Transactions on Information Theory | 0.3253 |
1b054a417f50ba51f9fac9e82a39a6f9d774504bd3502d9f3995f72012519355 | [
"arxiv",
"semantic_scholar"
] | Boosted Embeddings for Time Series Forecasting | Time series forecasting is a fundamental task emerging from diverse data-driven applications. Many advanced autoregressive methods such as ARIMA were used to develop forecasting models. Recently, deep learning based methods such as DeepAr, NeuralProphet, Seq2Seq have been explored for time series forecasting problem. I... | [
"Sankeerth Rao Karingula",
"Nandini Ramanan",
"Rasool Tahmasbi",
"Mehrnaz Amjadi",
"Deokwoo Jung",
"Ricky Si",
"Charanraj Thimmisetty",
"Luisa Polania Cabrera",
"Marjorie Sayer",
"Claudionor Nunes Coelho"
] | [
"cs.LG",
"cs.AI",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2021-04-10T00:00:00 | https://arxiv.org/abs/2104.04781 | https://arxiv.org/pdf/2104.04781v2 | 2104.04781 | 10.1007/978-3-030-95470-3_1 | 17 | 1 | false | null | International Conference on Machine Learning, Optimization, and Data Science | 0.3138 |
dbade99861019a3756df13218363dd4e55780884cafb055b356f3470ff6d7f27 | [
"arxiv",
"semantic_scholar"
] | Time Series Analysis and Modeling to Forecast: a Survey | Time series modeling for predictive purpose has been an active research area of machine learning for many years. However, no sufficiently comprehensive and meanwhile substantive survey was offered so far. This survey strives to meet this need. A unified presentation has been adopted for entire parts of this compilation... | [
"Fatoumata Dama",
"Christine Sinoquet"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2021-03-31T00:00:00 | https://arxiv.org/abs/2104.00164 | https://arxiv.org/pdf/2104.00164v2 | 2104.00164 | null | 39 | 0 | false | null | null | 0.4005 |
e03c67e740db87a9c61a7be6df0fcb75875bfd09b3e64ad766ab2406155c5c1e | [
"arxiv",
"semantic_scholar"
] | Hierarchical forecasting with a top-down alignment of independent level forecasts | Hierarchical forecasting with intermittent time series is a challenge in both research and empirical studies. Extensive research focuses on improving the accuracy of each hierarchy, especially the intermittent time series at bottom levels. Then hierarchical reconciliation could be used to improve the overall performanc... | [
"Matthias Anderer",
"Feng Li"
] | [
"stat.ML",
"cs.LG",
"stat.AP"
] | [
"Mathematics",
"Computer Science"
] | 2021-03-15T00:00:00 | https://arxiv.org/abs/2103.08250 | https://arxiv.org/pdf/2103.08250v4 | 2103.08250 | 10.1016/j.ijforecast.2021.12.015 | 21 | 0 | false | null | International Journal of Forecasting | 0.3356 |
1fd9ab3c6a3c586ed1afc58566269c4c8d13c16006534c1046d5ad813e8196ea | [
"arxiv",
"semantic_scholar"
] | Forecasting high-frequency financial time series: an adaptive learning approach with the order book data | This paper proposes a forecast-centric adaptive learning model that engages with the past studies on the order book and high-frequency data, with applications to hypothesis testing. In line with the past literature, we produce brackets of summaries of statistics from the high-frequency bid and ask data in the CSI 300 I... | [
"Parley Ruogu Yang"
] | [
"q-fin.ST",
"econ.EM",
"q-fin.TR",
"stat.AP"
] | [
"Computer Science",
"Economics",
"Mathematics"
] | 2021-02-27T00:00:00 | https://arxiv.org/abs/2103.00264 | https://arxiv.org/pdf/2103.00264v1 | 2103.00264 | 10.20944/PREPRINTS202103.0269.V1 | 3 | 0 | false | null | null | 0.1505 |
4616817c6a5b27b06738b89c15e51521a78f31fc8c437b33f09c822391cc7a63 | [
"arxiv",
"semantic_scholar"
] | NAST: Non-Autoregressive Spatial-Temporal Transformer for Time Series Forecasting | Although Transformer has made breakthrough success in widespread domains especially in Natural Language Processing (NLP), applying it to time series forecasting is still a great challenge. In time series forecasting, the autoregressive decoding of canonical Transformer models could introduce huge accumulative errors in... | [
"Kai Chen",
"Guang Chen",
"Dan Xu",
"Lijun Zhang",
"Yuyao Huang",
"Alois Knoll"
] | [
"cs.LG",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2021-02-10T00:00:00 | https://arxiv.org/abs/2102.05624 | https://arxiv.org/pdf/2102.05624v2 | 2102.05624 | null | 28 | 1 | false | null | arXiv.org | 0.3656 |
8dd00a2249f8dfe7305cbe6258791ed9a4076f80a13d211222ccc59dcf2bf45d | [
"arxiv",
"semantic_scholar"
] | Learning Interpretable Deep State Space Model for Probabilistic Time Series Forecasting | Probabilistic time series forecasting involves estimating the distribution of future based on its history, which is essential for risk management in downstream decision-making. We propose a deep state space model for probabilistic time series forecasting whereby the non-linear emission model and transition model are pa... | [
"Longyuan Li",
"Junchi Yan",
"Xiaokang Yang",
"Yaohui Jin"
] | [
"cs.LG",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2021-01-31T00:00:00 | https://arxiv.org/abs/2102.00397 | https://arxiv.org/pdf/2102.00397v1 | 2102.00397 | 10.24963/ijcai.2019/402 | 71 | 6 | false | null | International Joint Conference on Artificial Intelligence | 0.4643 |
7c7e107e5e6d0769aad861715c417df215e81b370e3e275305a14822aa8a9b25 | [
"arxiv",
"semantic_scholar"
] | Multi-Task Time Series Forecasting With Shared Attention | Time series forecasting is a key component in many industrial and business decision processes and recurrent neural network (RNN) based models have achieved impressive progress on various time series forecasting tasks. However, most of the existing methods focus on single-task forecasting problems by learning separately... | [
"Zekai Chen",
"Jiaze E",
"Xiao Zhang",
"Hao Sheng",
"Xiuzheng Cheng"
] | [
"cs.LG",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2021-01-24T00:00:00 | https://arxiv.org/abs/2101.09645 | https://arxiv.org/pdf/2101.09645v1 | 2101.09645 | 10.1109/ICDMW51313.2020.00132 | 27 | 3 | false | null | null | 0.3618 |
0aca506570b77208161b7bbc0f5e5d6edd94d0729a70612592ed480d40640878 | [
"arxiv",
"semantic_scholar"
] | A Trainable Reconciliation Method for Hierarchical Time-Series | In numerous applications, it is required to produce forecasts for multiple time-series at different hierarchy levels. An obvious example is given by the supply chain in which demand forecasting may be needed at a store, city, or country level. The independent forecasts typically do not add up properly because of the hi... | [
"Davide Burba",
"Trista Chen"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2021-01-05T00:00:00 | https://arxiv.org/abs/2101.01329 | https://arxiv.org/pdf/2101.01329v1 | 2101.01329 | null | 10 | 2 | false | null | arXiv.org | 0.2603 |
82ebb6afc23df529847b66f35268ad1557a8cafdb774eb2810ebe50f6e5795d8 | [
"arxiv",
"semantic_scholar"
] | Improving forecasting by subsampling seasonal time series | Time series forecasting plays an increasingly important role in modern business decisions. In today's data-rich environment, people often aim to choose the optimal forecasting model for their data. However, identifying the optimal model requires professional knowledge and experience, making accurate forecasting a chall... | [
"Xixi Li",
"Fotios Petropoulos",
"Yanfei Kang"
] | [
"stat.AP"
] | [
"Computer Science",
"Mathematics"
] | 2021-01-04T00:00:00 | https://arxiv.org/abs/2101.00827 | https://arxiv.org/pdf/2101.00827v4 | 2101.00827 | 10.1080/00207543.2021.2022800 | 14 | 1 | false | null | International Journal of Production Research | 0.294 |
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