nanoforecast-200k

<h1 align="center"> 🔮 NanoForecast 200k (v0.1) <br> _{World's smallest deployable time series transformer} </h1> <p align="center"> <b>676K params · 256 context · Just 2.7 MB</b> <br> <i>Fits on a Raspberry Pi Zero. Ships as a dependency, not an API call.</i> </p> <p align="center"> <a href="https://github.com/eulogik/NanoForecast"><img src="https://img.shields.io/badge/GitHub-eulogik%2FNanoForecast-181717?logo=github"></a> <a href="https://pypi.org/project/nanoforecast/"><img src="https://img.shields.io/pypi/v/nanoforecast"></a> <a href="https://eulogik.com"><img src="https://img.shields.io/badge/by-Eulogik-purple"></a> </p> <p align="center"> 💡 Need better accuracy? Check <a href="https://huggingface.co/eulogik/nanoforecast-500k"><b>nanoforecast-500k</b></a> (1.6M params, MASE 3.45) or the latest <a href="https://huggingface.co/eulogik/nanoforecast-v03"><b>nanoforecast-v03</b></a> (6.5M params, MASE 2.73). </p>

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📦 Quick Start

pip install nanoforecast
python3 train_from_csv.py --csv my_data.csv --target sales --horizon 48

Or use a pretrained model:

from nanoforecast import NanoForecast
model = NanoForecast.from_pretrained("eulogik/nanoforecast-200k")

📋 When to Use This Model

🔄 Streaming Inference

Feed one value at a time with DeltaNet stateful RNN:

result = model.predict(context, horizon=48, return_state=True)
state = result.pop("state")
for new_val in incoming_data_stream:
    result = model.predict_step(new_val, state, horizon=48)

📚 All Variants

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