| --- |
| license: mit |
| tags: |
| - time-series |
| - forecasting |
| - pytorch |
| - educational |
| --- |
| |
| # TinyForecaster |
|
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| A **~72,000-parameter educational time-series forecasting model**, built to understand — by actually building one — how zero-shot time-series foundation models work at a small scale. |
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| **This is not a production model.** It is not competitive with real time-series foundation models like Chronos, TimesFM, or [t0-alpha](https://huggingface.co/theforecastingcompany/t0-alpha). It was trained on 6,000 synthetic series for 20 epochs on a single CPU core. Full writeup, including an honest zero-shot benchmark against classical forecasting methods and a real foundation model, is in the [GitHub repo](https://github.com/Vedantl39/tiny-timeseries-foundation-model). |
|
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| ## What it does |
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| Given a 96-timestep context window, predicts the next 42 timesteps — trained **only on synthetic data** (randomized trend, seasonality, noise, scale), never on real data, then evaluated zero-shot on real retail sales data. |
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| ## Architecture |
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| A small patch-based transformer: |
| - Input context (96 steps) normalized by its own mean/std, split into 12 patches of 8 steps each |
| - 2-layer transformer encoder, 32-dim embeddings, 4 attention heads |
| - Linear head projects to a 42-step forecast, de-normalized back to the original scale |
|
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| ## Honest result |
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| Zero-shot MAPE on a real, held-out 42-day retail sales forecast: **24.87%** — worse than every classical method (SARIMA, Prophet, Holt-Winters) tested against the same data, and far behind t0-alpha's 6.53%. The specific failure mode (missing hard periodic zero-value drops, because the synthetic training data never included that pattern) is documented in the GitHub repo — it's a more useful finding than the raw accuracy number. |
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| ## Usage |
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| ```python |
| import torch |
| from model import TinyForecaster # see github.com/Vedantl39/tiny-timeseries-foundation-model |
| |
| model = TinyForecaster() |
| model.load_state_dict(torch.load("tiny_forecaster.pt")) |
| model.eval() |
| |
| context = torch.tensor([...]) # shape (1, 96), your last 96 observed values |
| forecast = model(context) # shape (1, 42) |
| ``` |
|
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| ## Author |
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| Vedant Limaye — built as a learning project after feedback from [Geoffrey Négiar](https://www.linkedin.com/in/geoffrey-negiar) (The Forecasting Company). |
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