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+*.safetensors filter=lfs diff=lfs merge=lfs -text

.gitignore

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+.ruff_cache/
+.venv/
+__pycache__/
+*.swp
+*.kate-swp
+*~

LICENSE

@@ -0,0 +1,474 @@
+Lowdown Labs Lovely License 1.0 (LLLL-1.0)
+
+Everything in this repository (the model weights, the configuration, and the code) is
+released by Lowdown Labs under two licenses that apply at the same time. To use this work
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+
+SPDX-License-Identifier: CC-BY-NC-4.0 AND LicenseRef-Hippocratic-3.0
+
+Commercial licensing. The grant below is non-commercial only. Commercial licenses are
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+Part 2; those apply to commercial licensees as well.
+
+==============================================================================
+Part 1 of 2. Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
+==============================================================================
+
+You may share and adapt this work for non-commercial purposes, with attribution to
+Lowdown Labs. Commercial use is not granted under this license.
+
+  Full legal text:        https://creativecommons.org/licenses/by-nc/4.0/legalcode
+  Plain-language summary:  https://creativecommons.org/licenses/by-nc/4.0/
+  SPDX-License-Identifier: CC-BY-NC-4.0
+
+==============================================================================
+Part 2 of 2. The Hippocratic License 3.0 (ethical use)
+==============================================================================
+
+Module set enabled: bds, cl, eco, extr, ffd, law, media, mil, my, soc, sup, sv, usta.
+Canonical build: https://firstdonoharm.dev/build/?modules=bds,cl,eco,extr,ffd,law,media,mil,my,soc,sup,sv,usta
+
+The verbatim official Hippocratic License 3.0 text for exactly this module set follows,
+between the markers.
+
+--------------------- BEGIN OFFICIAL HIPPOCRATIC LICENSE 3.0 TEXT ---------------------
+
+HIPPOCRATIC LICENSE
+
+Version 3.0, October 2021
+
+https://firstdonoharm.dev/version/3/0/bds-cl-eco-extr-ffd-law-media-mil-my-soc-sup-sv-usta.txt
+
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+   
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+      * 3.1.12.1. For the purpose of this section, “ecocide” means unlawful or
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+          (See Section II, Independent Expert Panel for the Legal Definition of
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+---------------------- END OFFICIAL HIPPOCRATIC LICENSE 3.0 TEXT ----------------------
+
+==============================================================================
+Attribution, commercial use, and warranty
+==============================================================================
+
+Attribution: cite this work as described in the model card (README.md), section
+"How to cite". Attribution to Lowdown Labs is required under CC BY-NC 4.0.
+
+Commercial use: CC BY-NC 4.0 does not grant commercial rights. Commercial licenses are
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+
+No warranty: this work is provided as is, without warranty of any kind. See the model card
+for the intended use, the evaluated conditions, and the known limitations.

README.md

@@ -0,0 +1,161 @@
+---
+license: other
+license_name: lowdown-labs-lovely-license-1.0
+license_link: LICENSE
+tags:
+  - fela
+  - fourier-neural-operator
+  - fno
+  - cpu
+  - on-device
+  - edge
+  - time-series
+  - forecasting
+  - energy
+  - electricity
+library_name: transformers
+pipeline_tag: time-series-forecasting
+---
+
+# DISCLAIMER
+
+This model is a research preview. It is released by Lowdown Labs in the interest of advancing
+public science and demonstrating that a Fourier Neural Operator can forecast on a plain CPU with
+a constant memory footprint. The dataset behind this
+one (UCI electricity load) is CC BY 4.0 and could permit commercial use with attribution; see the
+license notes below before deploying.
+
+# FELA-TS edge: On device electricity load forecaster
+
+FELA-TS edge reads a window of recent load history and forecasts the next 96 steps ahead. It is
+tiny, about 1.76M parameters, and runs on a plain CPU with no GPU. Its working memory does not grow with how long the stream has been running, so you can drop
+it into a meter or a building controller and let it forecast a live feed for years without the
+memory climbing due to the model expanding its usage.
+
+The shipped checkpoint is trained on the standard 321 channel electricity load benchmark, but the
+architecture underneath is a generic multi channel forecaster for long horizons.
+
+# What goes in, what comes out
+
+- Input: one history window of shape `(1, 512, 321)`: 512 past time steps across 321 load
+  channels, per channel standardized on the training statistics. RevIN handles the instance
+  normalization inside the model, so you pass the standardized history and the model manages the
+  per window mean and scale itself.
+- Output: a forecast of shape `(1, 96, 321)`: the next 96 steps for all 321 channels, returned in
+  the original (denormalized) units.
+- In plain terms: "here are the last 512 hours of load for these meters" goes in, and "here is the
+  next 96 hours for each of them" comes out.
+
+The edge/on device unit runs a single load stream at a time (one channel), which is the
+sub millisecond, constant memory path described under Performance.
+
+# Why we built it this way
+
+The sequence mixer is a Fourier Neural Operator, a filter the model learns and then applies in the
+frequency domain. Electricity load runs on strong daily and weekly cycles, and those cycles are
+what a frequency domain filter reads best, so the method fits the signal. The model is pure FNO
+with a patch embedding, and it normalizes each history window internally (a step called RevIN) so
+you do not have to hand tune it. It is pure FNO, with none of the gated memory or attention layers
+that some sibling FELA models add.
+
+Because it carries no attention and no KV cache, the working memory stays small and fixed no matter
+how much history has streamed through it. The live single stream path keeps its history in a 512
+sample ring buffer, about 2 KB, and one forward pass runs on roughly 126 KB of activations. That
+flat footprint is the whole point at the edge: a full history attention model would keep growing its
+memory until it ran out, while this one does not move.
+
+# Performance
+
+Speed and footprint, measured on CPU (single core, single load stream).
+
+| Format | Size on disk | Notes |
+|---|---|---|
+| fp32 | 9.96 MB | full model weights |
+| int8 (PyTorch/ONNX) | 7.29 MB | deployable edge unit |
+| TFLite float16 | 4.9 MB | smallest deployable export |
+
+- Full 321 channel forecast: median 165.7 ms on one CPU core.
+- Single channel edge unit forecast: median 0.863 ms on one CPU core.
+- int8 ONNX single stream update: about 0.56 ms per update (roughly 1,700 updates per second) on
+  one core.
+- Live working RAM (constant, independent of history length): a 2 KB ring buffer holds the entire
+  retained history, plus about 126 KB of activations, for a working set under 200 KB excluding
+  weights.
+
+Constant memory is verified: the O(1) ring buffer streaming path was checked against recomputing
+the forecast on the explicit window and matched exactly (mean absolute difference 0.0 over 1,800
+streamed steps). 
+
+The model fits comfortably on a Raspberry Pi 4/5 or Pi Zero 2 W (Cortex-A class,
+ONNX path); it probably could not fit a small microcontroller (the 5 to 7 MB model is larger than typical
+MCU flash) without explicitly targeting those constraints, so the realistic edge target is a Cortex-A single board computer, not a Cortex-M part.
+
+# Accuracy
+
+Protocol: UCI ElectricityLoadDiagrams20112014 ("electricity" / ECL benchmark), the standard
+Informer chronological 70/10/20 train/validation/test split, lookback L = 512, forecast horizon
+H = 96, over all 321 channels. Metric is mean squared error (MSE) and mean absolute error (MAE) on
+the z normalized test windows, the standard long horizon ECL protocol.
+
+| Benchmark | Metric | This model | Baseline (named) |
+|---|---|---|---|
+| Electricity, horizon 96 | MSE | 0.1325 | PatchTST ~0.129 |
+| Electricity, horizon 96 | MAE | 0.2233 | iTransformer MSE ~0.148, DLinear MSE ~0.140 |
+
+The card MSE/MAE of 0.1325 / 0.2233 reproduced at 0.1344 / 0.2254 on a fresh held out evaluation
+(1.76M parameters). PatchTST leads on accuracy by a small margin at this
+horizon; iTransformer and DLinear are behind. However, sub millisecond single stream forecasts on one CPU core with a flat, sub 200 KB working set are provably attainable, vs a full history attention model whose memory climbs with the length of the stream.
+
+# How to run it
+
+The short version:
+
+```python
+import torch
+from modeling import load_model, forecast
+
+m = load_model("/path/to/weights_dir")   # a dir with model.safetensors + config.json
+# x: a (1, 512, 321) history window, per channel standardized on training stats
+y = forecast(m, x)                        # -> (1, 96, 321) forecast in original units
+```
+
+`load_model` also accepts the `model.safetensors` path directly or a Hugging Face repo id. The fp32
+weights ship as `model.safetensors`; the loader builds the architecture from `config.json` and
+loads the state dict.
+
+# Training data and license
+
+- Dataset: UCI ElectricityLoadDiagrams20112014 ("electricity" / ECL), the 321 client hourly
+  variant used across the Informer / Autoformer / PatchTST time series literature (26,304 hourly
+  timesteps x 321 client load streams). Source: UCI Machine Learning Repository, dataset 321
+  (DOI 10.24432/C58C86). The training split, windowing, and the audited train window count are
+  reproduced in `train.py` (`--smoke` rebuilds the split and asserts 17,805 train windows).
+- License: Creative Commons Attribution 4.0 International (CC BY 4.0), as listed on the UCI dataset
+  page. Commercial use is ALLOWED, contingent only on attribution and preserving the license terms.
+  This is the one dataset in the FELA industrial family with an unambiguous, commercially usable
+  license. Attribution: cite the UCI ElectricityLoadDiagrams20112014 dataset (DOI 10.24432/C58C86).
+
+# Intended use, limitations, and safety
+
+What it is for: the forecasting core inside an edge or on premises energy product, running on a CPU
+or a single board computer. Fit includes live load forecasting on a meter or gateway, building and
+microgrid controllers, and any long running multi channel forecast where the memory must stay flat
+while history accumulates.
+
+What it is not for: this is a forecasting model, not a control system or a guarantee. It was
+trained and evaluated only on the UCI electricity benchmark; generalization to other load profiles,
+sampling rates, sensors, and populations is not established and must be validated before any
+operational use. Accuracy is at parity with, not ahead of, the strongest published transformer
+forecaster (PatchTST leads by a small margin); the model wins on footprint and constant memory, not
+on top line accuracy. Continuous stream benchmarks beyond the verified streaming equals batch check
+(for example multi day real time factor on a specific physical board) are not yet measured.
+
+# Model family
+
+This is part of the FELA family from Lowdown Labs: one Fourier Neural Operator architecture across
+many modalities, all CPU native and subquadratic. Sibling repos share no weights, so none carries a
+`base_model` link.
+
+# License
+
+Released under the Lowdown Labs Lovely License 1.0 (CC BY-NC 4.0 plus Hippocratic License 3.0). See LICENSE. For most LL models, a commercial license may be available; contact Lowdown Labs.

config.json

@@ -0,0 +1,32 @@
+{
+  "model_type": "fela-ts",
+  "architectures": [
+    "FelaTsModel"
+  ],
+  "auto_map": {
+    "AutoConfig": "configuration_ts.FelaTsConfig",
+    "AutoModel": "modeling_ts.FelaTsModel"
+  },
+  "library_name": "pytorch",
+  "arch": "FELA_TS",
+  "note": "FELA time series forecaster, about 1.76M params. RevIN, patch embedding, FNO spectral mixer blocks, linear head. Trained on electricity, 321 channels, L=512, H=96.",
+  "C": 321,
+  "L": 512,
+  "H": 96,
+  "patch": 16,
+  "stride": 8,
+  "D": 128,
+  "modes": 16,
+  "nblk": 3,
+  "input_shape": [
+    1,
+    512,
+    321
+  ],
+  "input_desc": "history window shape (1, 512, 321): 512 past steps of 321 channels, standardized per channel on training statistics. RevIN normalizes instances inside the model.",
+  "complex_keys": [
+    "blocks.0.fno.w",
+    "blocks.1.fno.w",
+    "blocks.2.fno.w"
+  ]
+}

configuration_ts.py

@@ -0,0 +1,27 @@
+from transformers import PretrainedConfig
+
+
+class FelaTsConfig(PretrainedConfig):
+    model_type = "fela-ts"
+
+    def __init__(
+        self,
+        C=321,
+        L=512,
+        H=96,
+        patch=16,
+        stride=8,
+        D=128,
+        modes=16,
+        nblk=3,
+        **kwargs,
+    ):
+        self.C = C
+        self.L = L
+        self.H = H
+        self.patch = patch
+        self.stride = stride
+        self.D = D
+        self.modes = modes
+        self.nblk = nblk
+        super().__init__(**kwargs)

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4f4fbc05634862cbdf2cc8bc35906426785750d491b71d25cae39ea20389b0be
+size 10199640

modeling.py

@@ -0,0 +1,157 @@
+from __future__ import annotations
+import json
+import os
+from dataclasses import dataclass
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+@dataclass
+class TSConfig:
+    C: int = 321
+    L: int = 512
+    H: int = 96
+    patch: int = 16
+    stride: int = 8
+    D: int = 128
+    modes: int = 16
+    nblk: int = 3
+
+
+class RevIN(nn.Module):
+    def __init__(self, C):
+        super().__init__()
+        self.g = nn.Parameter(torch.ones(C))
+        self.b = nn.Parameter(torch.zeros(C))
+
+    def norm(self, x):
+        self.m = x.mean(1, keepdim=True)
+        self.s = x.std(1, keepdim=True) + 1e-05
+        return (x - self.m) / self.s * self.g + self.b
+
+    def denorm(self, x):
+        return (x - self.b) / self.g * self.s + self.m
+
+
+class FNO1D(nn.Module):
+    def __init__(self, D, m):
+        super().__init__()
+        self.m = m
+        self.w = nn.Parameter(1 / (D * D) * torch.rand(m, D, D, dtype=torch.cfloat))
+
+    def forward(self, x):
+        P = x.shape[1]
+        xf = torch.fft.rfft(x, dim=1)
+        mm = min(self.m, xf.shape[1])
+        o = torch.zeros_like(xf)
+        o[:, :mm] = torch.einsum("bpd,pde->bpe", xf[:, :mm], self.w[:mm])
+        return torch.fft.irfft(o, n=P, dim=1)
+
+
+class Block(nn.Module):
+    def __init__(self, D, m, ff=2, drop=0.2):
+        super().__init__()
+        self.n1 = nn.LayerNorm(D)
+        self.fno = FNO1D(D, m)
+        self.d1 = nn.Dropout(drop)
+        self.n2 = nn.LayerNorm(D)
+        self.ff = nn.Sequential(
+            nn.Linear(D, D * ff), nn.GELU(), nn.Dropout(drop), nn.Linear(D * ff, D)
+        )
+
+    def forward(self, x):
+        x = x + self.d1(self.fno(self.n1(x)))
+        return x + self.ff(self.n2(x))
+
+
+class FELA_TS(nn.Module):
+    def __init__(self, C, L, H, patch=16, stride=8, D=128, modes=16, nblk=3):
+        super().__init__()
+        self.C, self.L, self.H, self.patch, self.stride = (C, L, H, patch, stride)
+        self.revin = RevIN(C)
+        self.np_ = (L - patch) // stride + 1
+        self.embed = nn.Linear(patch, D)
+        self.blocks = nn.ModuleList([Block(D, modes) for _ in range(nblk)])
+        self.head = nn.Linear(self.np_ * D, H)
+
+    def forward(self, x):
+        x = self.revin.norm(x)
+        x = x.permute(0, 2, 1).reshape(-1, self.L)
+        x = x.unfold(1, self.patch, self.stride)
+        h = self.embed(x)
+        for b in self.blocks:
+            h = b(h)
+        y = self.head(h.flatten(1)).reshape(-1, self.C, self.H).permute(0, 2, 1)
+        return self.revin.denorm(y)
+
+
+_CONFIG_FIELDS = set(TSConfig.__dataclass_fields__.keys())
+
+
+def _read_json(path):
+    with open(path) as f:
+        return json.load(f)
+
+
+def _cfg_from_dict(d):
+    return TSConfig(**{k: v for k, v in d.items() if k in _CONFIG_FIELDS})
+
+
+def validate_history(x: torch.Tensor, cfg: TSConfig):
+    if x.dim() != 3:
+        raise ValueError(f"expected a 3D tensor (B, L, C); got {tuple(x.shape)}")
+    if x.size(1) != cfg.L or x.size(2) != cfg.C:
+        raise ValueError(
+            f"expected history of shape (B, {cfg.L}, {cfg.C}); got {tuple(x.shape)}"
+        )
+
+
+def load_model(path_or_repo: str):
+    if os.path.isdir(path_or_repo):
+        cfg_dict = _read_json(os.path.join(path_or_repo, "config.json"))
+        weights = os.path.join(path_or_repo, "model.safetensors")
+    elif os.path.isfile(path_or_repo) and path_or_repo.endswith(".safetensors"):
+        cfg_dict = _read_json(
+            os.path.join(os.path.dirname(path_or_repo), "config.json")
+        )
+        weights = path_or_repo
+    elif os.path.isfile(path_or_repo):
+        cfg_dict = _read_json(
+            os.path.join(os.path.dirname(path_or_repo) or ".", "config.json")
+        )
+        weights = path_or_repo
+    else:
+        from huggingface_hub import hf_hub_download
+
+        cfg_dict = _read_json(hf_hub_download(path_or_repo, "config.json"))
+        weights = hf_hub_download(path_or_repo, "model.safetensors")
+    cfg = _cfg_from_dict(cfg_dict)
+    model = FELA_TS(
+        cfg.C, cfg.L, cfg.H, cfg.patch, cfg.stride, cfg.D, cfg.modes, cfg.nblk
+    ).eval()
+    if weights.endswith(".safetensors"):
+        from safetensors.torch import load_file
+
+        state = load_file(weights)
+        cplx = set(cfg_dict.get("complex_keys", []))
+        state = {
+            k: (torch.view_as_complex(v.contiguous()) if k in cplx else v)
+            for k, v in state.items()
+        }
+    else:
+        state = torch.load(weights, map_location="cpu", weights_only=False)
+        if isinstance(state, dict) and "state_dict" in state:
+            state = state["state_dict"]
+    model.load_state_dict(state)
+    model.cfg = cfg
+    return model
+
+
+from_pretrained = load_model
+
+
+@torch.no_grad()
+def forecast(model, x: torch.Tensor) -> torch.Tensor:
+    validate_history(x, model.cfg)
+    return model(x)

modeling_ts.py

@@ -0,0 +1,56 @@
+import os
+import sys
+import types
+
+sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
+import torch
+import torch.nn as nn
+from transformers import PreTrainedModel
+from transformers.modeling_outputs import CausalLMOutput
+
+from .configuration_ts import FelaTsConfig
+from .modeling import FELA_TS, FNO1D
+
+
+def _fno1d_forward(self, x):
+    w = torch.view_as_complex(self.w)
+    P = x.shape[1]
+    xf = torch.fft.rfft(x, dim=1)
+    mm = min(self.m, xf.shape[1])
+    o = torch.zeros_like(xf)
+    o[:, :mm] = torch.einsum("bpd,pde->bpe", xf[:, :mm], w[:mm])
+    return torch.fft.irfft(o, n=P, dim=1)
+
+
+def _realify(model):
+    for m in model.modules():
+        if isinstance(m, FNO1D):
+            m.w = nn.Parameter(torch.view_as_real(m.w.detach()).contiguous())
+            m.forward = types.MethodType(_fno1d_forward, m)
+
+
+class FelaTsModel(PreTrainedModel):
+    config_class = FelaTsConfig
+    base_model_prefix = "model"
+    main_input_name = "x"
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = FELA_TS(
+            config.C,
+            config.L,
+            config.H,
+            config.patch,
+            config.stride,
+            config.D,
+            config.modes,
+            config.nblk,
+        )
+        _realify(self.model)
+        self.post_init()
+
+    def forward(self, x=None, input_values=None, **kwargs):
+        if x is None:
+            x = input_values
+        out = self.model(x)
+        return CausalLMOutput(logits=out)

streaming/manifest.json

@@ -0,0 +1,14 @@
+{
+  "model": "ts-edge",
+  "format": "fp16-streaming",
+  "note": "load order is smallest-first for progressive/streaming load",
+  "files": [
+    {
+      "file": "model_fp16.safetensors",
+      "source": "model.safetensors",
+      "dtype": "fp16",
+      "bytes": 5101188,
+      "approx_mb": 4.865
+    }
+  ]
+}

streaming/model_fp16.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:11ca25fba43652d89559fc17bd610ded2f920b2cb742046bc3d7482f57e59332
+size 5101188

train.py

@@ -0,0 +1,137 @@
+import sys, numpy as np, pandas as pd, torch, torch.nn as nn, torch.nn.functional as F
+
+dev = "cuda" if torch.cuda.is_available() else "cpu"
+torch.manual_seed(0)
+np.random.seed(0)
+csv, L, H = ("/workspace/data/electricity.csv", 512, 96)
+smoke = "--smoke" in sys.argv
+save = (
+    sys.argv[sys.argv.index("--save") + 1]
+    if "--save" in sys.argv
+    else "/workspace/ts_demo/fela_ts_electricity.pt"
+)
+epochs = int(sys.argv[sys.argv.index("--epochs") + 1]) if "--epochs" in sys.argv else 30
+
+
+class RevIN(nn.Module):
+    def __init__(s, C):
+        super().__init__()
+        s.g = nn.Parameter(torch.ones(C))
+        s.b = nn.Parameter(torch.zeros(C))
+
+    def norm(s, x):
+        s.m = x.mean(1, keepdim=True)
+        s.s = x.std(1, keepdim=True) + 1e-05
+        return (x - s.m) / s.s * s.g + s.b
+
+    def denorm(s, x):
+        return (x - s.b) / s.g * s.s + s.m
+
+
+class FNO1D(nn.Module):
+    def __init__(s, D, m):
+        super().__init__()
+        s.m = m
+        s.w = nn.Parameter(1 / (D * D) * torch.rand(m, D, D, dtype=torch.cfloat))
+
+    def forward(s, x):
+        P = x.shape[1]
+        xf = torch.fft.rfft(x, dim=1)
+        mm = min(s.m, xf.shape[1])
+        o = torch.zeros_like(xf)
+        o[:, :mm] = torch.einsum("bpd,pde->bpe", xf[:, :mm], s.w[:mm])
+        return torch.fft.irfft(o, n=P, dim=1)
+
+
+class Block(nn.Module):
+    def __init__(s, D, m, ff=2, drop=0.2):
+        super().__init__()
+        s.n1 = nn.LayerNorm(D)
+        s.fno = FNO1D(D, m)
+        s.d1 = nn.Dropout(drop)
+        s.n2 = nn.LayerNorm(D)
+        s.ff = nn.Sequential(
+            nn.Linear(D, D * ff), nn.GELU(), nn.Dropout(drop), nn.Linear(D * ff, D)
+        )
+
+    def forward(s, x):
+        x = x + s.d1(s.fno(s.n1(x)))
+        return x + s.ff(s.n2(x))
+
+
+class FELA_TS(nn.Module):
+    def __init__(s, C, L, H, patch=16, stride=8, D=128, modes=16, nblk=3):
+        super().__init__()
+        s.C, s.L, s.H, s.patch, s.stride = (C, L, H, patch, stride)
+        s.revin = RevIN(C)
+        s.np_ = (L - patch) // stride + 1
+        s.embed = nn.Linear(patch, D)
+        s.blocks = nn.ModuleList([Block(D, modes) for _ in range(nblk)])
+        s.head = nn.Linear(s.np_ * D, H)
+
+    def forward(s, x):
+        x = s.revin.norm(x)
+        x = x.permute(0, 2, 1).reshape(-1, s.L)
+        x = x.unfold(1, s.patch, s.stride)
+        h = s.embed(x)
+        for b in s.blocks:
+            h = b(h)
+        y = s.head(h.flatten(1)).reshape(-1, s.C, s.H).permute(0, 2, 1)
+        return s.revin.denorm(y)
+
+
+df = pd.read_csv(csv)
+cols = [c for c in df.columns if c != "date"]
+data = df[cols].values.astype(np.float32)
+n = len(data)
+ntr, nva = (int(n * 0.7), int(n * 0.1))
+mu = data[:ntr].mean(0)
+sd = data[:ntr].std(0) + 1e-08
+data = (data - mu) / sd
+
+
+def win(a):
+    xs, ys = ([], [])
+    for i in range(0, len(a) - L - H + 1, 1):
+        xs.append(a[i : i + L])
+        ys.append(a[i + L : i + L + H])
+    return (torch.tensor(np.array(xs)), torch.tensor(np.array(ys)))
+
+
+Xtr, Ytr = win(data[:ntr])
+Xte, Yte = win(data[ntr + nva :])
+C = data.shape[1]
+assert len(Xtr) == 17805
+if smoke:
+    print(
+        f"Electricity C={C} n={n} ntr={ntr} nva={nva} train {len(Xtr)} test {len(Xte)}"
+    )
+    sys.exit()
+m = FELA_TS(C, L, H).to(dev)
+opt = torch.optim.Adam(m.parameters(), lr=0.001)
+sch = torch.optim.lr_scheduler.CosineAnnealingLR(opt, epochs)
+bs = 64
+print(f"[Ts] electricity C={C} train {len(Xtr)} test {len(Xte)}")
+for ep in range(epochs):
+    m.train()
+    p = torch.randperm(len(Xtr))
+    for i in range(0, len(Xtr) - bs, bs):
+        idx = p[i : i + bs]
+        loss = F.l1_loss(m(Xtr[idx].to(dev)), Ytr[idx].to(dev))
+        opt.zero_grad()
+        loss.backward()
+        opt.step()
+    sch.step()
+m.eval()
+se = ae = cnt = 0
+with torch.no_grad():
+    for i in range(0, len(Xte), 256):
+        pr = m(Xte[i : i + 256].to(dev))
+        y = Yte[i : i + 256].to(dev)
+        se += F.mse_loss(pr, y, reduction="sum").item()
+        ae += (pr - y).abs().sum().item()
+        cnt += y.numel()
+mse, mae = (se / cnt, ae / cnt)
+print(f"[Ts] electricity/96 TEST MSE {mse:.4f} MAE {mae:.4f}")
+torch.save(m.state_dict(), save)
+print(f"SAVED {save}")