nanoforecast-v03

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NanoForecast v0.3

6.5M-Parameter Time Series Foundation Model — Deploy Anywhere

CPU inference · Raspberry Pi · ONNX · Streaming · Quantile forecasts

Built by Eulogik — deployable AI for the real world

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What is NanoForecast v0.3?

NanoForecast v0.3 is the initial release of a 6.5M-parameter time series foundation model designed for deployment on CPUs, Raspberry Pi, edge devices, and in the browser. It performs zero-shot forecasting with quantile uncertainty bounds.

This is a reference release. For the latest improvements, see eulogik/nanoforecast-v05 (43.8% better MASE with same architecture).

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Key Features

• Zero-shot forecasting — no training needed for new time series
• Streaming inference — feed one value at a time via stateful DeltaNet RNN
• Quantile predictions — p10, p25, p50, p75, p90 with monotonic guarantees
• ONNX export — 9.2MB INT8 for edge, IoT, browser deployment
• CPU inference — 19.5ms median latency on Apple M4
• Train from CSV — fine-tune on your data in minutes
• Apache 2.0 license — no restrictions on commercial use

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Benchmark Results

Standard protocol: context 512, horizon 48, non-overlapping test windows, MASE scaled by seasonal-naive in-sample MAE.

v0.3 → v0.5 improvement: Overall MASE 3.030 → 1.704 (↓43.8%) with same architecture, pipeline fixes only.

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Quick Start

Install

pip install nanoforecast

Zero-Shot Forecasting

import numpy as np
from nanoforecast import NanoForecast

model = NanoForecast.from_pretrained("eulogik/nanoforecast-v03")

context = np.sin(np.linspace(0, 8*np.pi, 512)) + 0.1 * np.random.randn(512)
result = model.predict(context, horizon=48, freq=1)

print(result["forecast"].shape)     # (48,) point forecast
print(result["quantiles"].shape)    # (5, 48)  p10..p90

Streaming Inference

result = model.predict(context, horizon=48, return_state=True)
state = result.pop("state")

for new_val in incoming_stream:
    result = model.predict_step(new_val, state, horizon=48)
    forecast = result["forecast"][0]

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Model Files

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Citation

@article{kishore2026nanoforecast,
  title={NanoForecast v0.5: Competitive Time Series Forecasting Through Training Pipeline Optimization},
  author={Kishore, Gautam},
  journal={arXiv preprint arXiv:2609.31669},
  year={2026}
}

Paper: arxiv.org/abs/2609.31669

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Links

• GitHub: github.com/eulogik/NanoForecast
• Live Demo: huggingface.co/spaces/eulogik/nanoforecast
• Paper: arxiv.org/abs/2609.31669
• Latest version: eulogik/nanoforecast-v05
• Paper: arxiv.org/abs/2609.31669
• Website: eulogik.com

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Built by Eulogik — deployable AI for the real world

If you found this useful, please ⭐ the GitHub repo and like this model on Hugging Face!

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