| --- |
| license: cc-by-nc-4.0 |
| library_name: pytorch |
| pipeline_tag: time-series-forecasting |
| tags: |
| - time-series |
| - forecasting |
| - foundation-model |
| - zero-shot |
| - probabilistic-forecasting |
| - gift-eval |
| --- |
| |
| # Modular-TS-2.4M |
|
|
| **A 2.4M-parameter time-series foundation model, pretrained from scratch by [Modular Computing](https://huggingface.co/Modularcomputing).** |
|
|
| Modular-TS-2.4M is a small, fast, zero-shot probabilistic forecaster: give it any univariate or multivariate series and it returns 21 quantiles for every future step, with no fine-tuning. It is built for the GPU-poor: it runs comfortably on a CPU or a phone-class device. |
|
|
| ## Results (GIFT-Eval, full 97 configurations, zero-shot) |
|
|
| Geometric mean relative to Seasonal Naive (lower is better). |
|
|
| | Model | Params | MASE | CRPS | |
| |---|---|---|---| |
| | **Modular-TS-2.4M (ours)** | **2.41M** | **0.745** | **0.511** | |
| | IBM TTM-R3 (PT) | ~1–5M | 0.724 | 0.520 | |
| | TimeSSM-2.5M | 2.5M | 0.754 | 0.513 | |
| | Seasonal Naive | – | 1.000 | 1.000 | |
|
|
| - Best CRPS (probabilistic accuracy) of any open model at 2.5M parameters or fewer that we evaluated. |
| - Beats TimeSSM-2.5M on both metrics and TTM-R3 on CRPS; TTM-R3 is still ahead on MASE. |
| - Our numbers are strictly zero-shot: the model never sees a test dataset's own training split at inference time. (TTM-R3-PT uses each dataset's train split for backtest ensembling.) |
| - Evaluated with the official GIFT-Eval harness at leaderboard settings; the same harness reproduces published leaderboard scores for reference models exactly. |
|
|
| ## Model |
|
|
| | | | |
| |---|---| |
| | Architecture | Decoder-style patch transformer (Timestral-style), d_model 128, 2 heads, MLP 320, 8 layers alternating time and variate attention, patch 32 | |
| | Parameters | 2,407,680 (embed 107,392 / trunk 1,804,672 / head 495,616) | |
| | Context | up to 8,192 steps | |
| | Output | 21 monotone quantiles per step (plus mean) | |
| |
| The architecture code adapts layers from Mistral AI's Apache-2.0 `timestral` inference package. **No Timestral weights were loaded, fine-tuned, or distilled at any point.** Every weight in this repo was trained from random initialisation. |
| |
| ## Training data |
| |
| - **Synthetic (majority):** a GPU-vectorised generator with Gaussian-process kernels, regime-switching Ornstein–Uhlenbeck processes, waveform and load-curve templates, steps, growth curves, intermittent counts, multivariate lead-lag mixing, covariates and missingness, followed by an augmentation cascade. |
| - **Real (~40% in the final stage):** GiftEvalPretrain, resolution-sampled with TSMixup, after a contamination filter that removes any dataset overlapping the GIFT-Eval test sets. |
| |
| ## Usage |
| |
| ```python |
| from huggingface_hub import snapshot_download |
| path = snapshot_download("Modularcomputing/Modular-TS-2.4M") |
| # path/export/model.safetensors + path/export/config.json |
| # Load with the bundled modular_ts package (see modular_ts/README.md), |
| # which wraps the Apache-2.0 timestral forecaster around these weights. |
| ``` |
| |
| Files: |
| - `export/model.safetensors`, `export/config.json`: inference weights and config |
| - `modular_ts/`: training, synthetic data generation and evaluation code |
| - `results/all_results.csv`: per-configuration GIFT-Eval scores |
| |
| ## Licence |
| |
| - **Weights:** CC BY-NC 4.0 (research and non-commercial use), because the real-data portion of training mixes datasets under several licences. |
| - **Code (`modular_ts/`):** Apache-2.0, including the adapted `timestral` components (see `modular_ts/NOTICE`). |
| |
| ## Limitations |
| |
| - Trails TTM-R3 on point accuracy (MASE); weakest on some long-horizon, high-frequency energy and weather series. |
| - A small model: it captures common seasonal and trend structure well but can miss rare regime changes. |
| |
| ## Citation |
| |
| ```bibtex |
| @misc{modularts2026, |
| title = {Modular-TS-2.4M: a small time-series foundation model trained from scratch}, |
| author = {Akuegbo, Chima Emmanuel and Modular Computing}, |
| year = {2026}, |
| url = {https://huggingface.co/Modularcomputing/Modular-TS-2.4M} |
| } |
| ``` |
| |
