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Sponsored by
Vmake Labs Video Enhancer
eulogik / nanoforecast-200k
Model's Last Updated: June 27 2026
time-series-forecasting
Introduction of nanoforecast-200k
Model Details of nanoforecast-200k
nanoforecast-200k
NanoForecast is the world's most deployable time series forecasting model (~676K parameters). It trains on a laptop, runs on a Raspberry Pi, and exports to 1.4 MB ONNX for edge/IoT/browser deployment.
Model details
-
Profile
:
nano-200k - Parameters : 676,108
- Context length : 256
- Prediction length : 48
- Patch size : 8
- Hidden dim / layers : 32 / 4
- Quantiles : [0.1, 0.25, 0.5, 0.75, 0.9]
- Architecture : LongConv + DeltaNet RNN + Gated Router + MLP
Deploy
# FastAPI server
pip install nanoforecast fastapi uvicorn python-multipart
python3 deploy/fastapi_server.py
# Docker
docker build -t nanoforecast -f deploy/Dockerfile .
docker run -p 8000:8000 nanoforecast
# ONNX export (1.4 MB)
pip install "nanoforecast[onnx]"
python3 -m nanoforecast.export.onnx_export --checkpoint checkpoints/nanoforecast-200k
Training
- Datasets : ETTh1
- Epochs : 20
- Learning rate : 0.0001
- Batch size : 32
- Best epoch : 9 (val_loss=11.2837)
- Wall time : 48.2s
Benchmarks
| Dataset | MASE | sMAPE (%) | MAE | CRPS |
|---|---|---|---|---|
| ETTh1 | 4.662 | 35.70 | 3.365 | 2.158 |
| ETTh2 | 7.073 | 41.78 | 6.384 | 3.822 |
| ETTm1 | 8.285 | 35.62 | 2.762 | 1.881 |
| exchange_rate | 10.870 | 2.36 | 0.015 | 0.010 |
| overall | 7.723 | 28.87 | 3.132 | 1.968 |
Quickstart
import numpy as np
from nanoforecast import NanoForecast
model = NanoForecast.from_pretrained('eulogik/nanoforecast-200k')
context = np.sin(np.linspace(0, 8*np.pi, 256)) + 0.1 * np.random.randn(256)
out = model.predict(context, horizon=48, freq=1)
print(out['forecast'].shape) # (48,) point forecast
print(out['quantiles'].shape) # (5, 48) p10..p90
Try it in a browser
Upload your CSV to the [Gradio Space]( https://huggingface.co/spaces/ eulogik/nanoforecast) and get a forecast in seconds.
Known limitations
This checkpoint was trained on a single dataset (ETTh1, ~1000 windows, 20 epochs). It is not a production foundation model. Accuracy is limited (MASE 4-11 on ETT). What it does well: being deployable. Train your own for better accuracy.
Runs of eulogik nanoforecast-200k on huggingface.co
22
Total runs
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24-hour runs
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3-day runs
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7-day runs
14
30-day runs
More Information About nanoforecast-200k huggingface.co Model
Url of nanoforecast-200k
nanoforecast-200k huggingface.co Url
Provider of nanoforecast-200k huggingface.co
Other API from eulogik
Updated:September 23 2026
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