nanoforecast-500k

eulogik/nanoforecast-500k

nanoforecast-500k is a time series forecasting model from eulogik. Use it for the time series forecasting task on the model card, and read the license before you ship it in a product. It is set up for pytorch. The card lists the license as apache-2.0.

<h1 align="center" 🔮 NanoForecast 500k (v0.2) <br <subUltra-lightweight time series transformer</sub </h1

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At a glance

Task: Time Series Forecasting
Library: pytorch
License: apache-2.0
Access: Public
Created: Jun 19, 2026
Updated: Jun 27, 2026
SHA: 98e273ae

Model card

From the Hugging Face model README

<h1 align="center"> 🔮 NanoForecast 500k (v0.2) <br> <sub>Ultra-lightweight time series transformer</sub> </h1> <p align="center"> <b>1.6M params · 256 context · Streaming RNN · ONNX-ready</b> <br> <i>Runs on CPU, Raspberry Pi, and in the browser</i> </p> <p align="center"> <a href="https://huggingface.co/spaces/eulogik/nanoforecast"><img src="https://img.shields.io/badge/🤗%20Live%20Demo-Spaces-blueviolet"></a> <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"> 🚀 <b>New:</b> Try the improved <a href="https://huggingface.co/eulogik/nanoforecast-v03"><b>nanoforecast-v03</b></a> — 21% better MASE, 6.5M params, 512 context! </p>

📦 Quick Start

pip install nanoforecast

from nanoforecast import NanoForecast
model = NanoForecast.from_pretrained("eulogik/nanoforecast-500k")

🏆 Benchmarks

Dataset	MASE	sMAPE (%)	MAE	CRPS
ETTh1	3.342	25.13	2.402	1.800
ETTh2	3.707	17.65	3.212	2.518
ETTm1	3.578	17.22	1.174	1.003
exchange_rate	7.306	1.63	0.010	0.009
electricity	1.536	5.65	189.748	187.256
traffic	1.246	44.80	0.006	0.005
Overall	3.453	18.68	32.759	32.099

🔥 For better accuracy, upgrade to nanoforecast-v03 (MASE 2.73, 21% improvement).

🔄 Streaming Inference (Unique to NanoForecast)

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)
    print(result["forecast"][0, :5])

Perfect for IoT, real-time dashboards, and live financial data.

📋 Model Details

Attribute	Value
Profile	`d64-L8`
Parameters	1,606,232
Context	256
Horizon	48
Size	6.4 MB (FP32), ~1.4 MB (ONNX)
Architecture	LongConv + DeltaNet RNN + Gated Router + MLP
Deploy targets	CPU, ARM, Raspberry Pi, Lambda, iOS, browser

🎯 Try It in 1 Click

Upload a CSV → forecast + prediction intervals. No code. No GPU.

📚 All Variants

Model	Params	Context	MASE	Best For
nanoforecast-200k	676K	256	~4-11	Extreme edge / RPi Zero
nanoforecast-500k (you are here)	1.6M	256	3.45	General purpose
nanoforecast-v03	6.5M	512	2.73	Max accuracy

⚡ Deploy

# FastAPI
pip install nanoforecast fastapi uvicorn python-multipart
python3 deploy/fastapi_server.py

# ONNX
pip install "nanoforecast[onnx]"
python3 -m nanoforecast.export.onnx_export --checkpoint <dir> --output nanoforecast.onnx

❤️ Built by Eulogik

Eulogik — deployable AI for the real world.

Star the repo ⭐ on GitHub!

Task: Time Series Forecasting
Library: pytorch
License: apache-2.0
Created: Jun 19, 2026
Updated: Jun 27, 2026