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nanoforecast-v05

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NanoForecast v0.5

6.5M-Parameter Time Series Foundation Model — Deploy Anywhere

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

GitHub

Open in Colab

Built by Eulogik — deployable AI for the real world

What is NanoForecast?

NanoForecast is a 6.5M-parameter time series foundation model that runs inference on CPUs, Raspberry Pi, edge devices, and in the browser. It performs zero-shot forecasting on unseen time series without fine-tuning, producing point forecasts with quantile uncertainty bounds (p10–p90).

Unlike 200M+ parameter alternatives (TimesFM, Chronos), NanoForecast is designed for deployment constraints: 19.5ms CPU inference, ONNX export (9.2MB INT8), streaming RNN mode, and Apache 2.0 license. It matches or beats TimesFM on 4 of 6 standard benchmarks at 31x fewer parameters.

Key Features

Zero-shot forecasting — no training needed for new time series
Streaming inference — feed one value at a time via stateful DeltaNet RNN (unique to NanoForecast)
Quantile predictions — p10, p25, p50, p75, p90 with monotonic guarantees
ONNX export — 9.2MB INT8 / 27.9MB FP32 for edge, IoT, browser deployment
CPU inference — 19.5ms median latency on Apple M4 (no GPU required)
Train from CSV — fine-tune on your data in minutes, not days
Apache 2.0 license — no restrictions on commercial use
Multi-task heads — point forecast + quantiles + anomaly detection in single forward pass

Benchmark Results

Standard protocol: context 512, horizon 48, non-overlapping test windows, MASE scaled by seasonal-naive in-sample MAE. All models evaluated under identical conditions.

Dataset	NanoForecast v0.5 (6.5M)	TimesFM (200M)	PatchTST (15M+)
ETTh1	0.681	0.705	0.781
ETTh2	1.110	1.360	1.467
ETTm1	0.287	0.545	0.488
exchange_rate	4.317	4.383	3.861
electricity	2.029	0.923	1.347
traffic	1.805	0.765	1.379
Overall MASE	1.704	1.447	1.554

Results: NanoForecast v0.5 beats TimesFM on 4 of 6 benchmarks (ETTh1, ETTh2, ETTm1, exchange_rate) at 31x fewer parameters. TimesFM wins on electricity and traffic.

Parameter Efficiency

NanoForecast achieves 36x better efficiency (MASE per billion parameters) than TimesFM and is 2x more efficient than PatchTST.

Head-to-Head Wins

Training-Pipeline Refinement: v0.3 → v0.5

The same 6.5M-parameter architecture gained 43.8% better MASE through three training-pipeline fixes — no architecture changes.

Version	Params	MASE ↓	Improvement	Training
v0.3 (released)	6.5M	3.030	baseline	Colab T4, 200 epochs
v0.5 (released)	6.5M	1.704	↓ 43.8%	Colab T4, 200 epochs

Quantile Calibration

NanoForecast produces well-calibrated uncertainty estimates. Coverage of predicted quantiles closely matches targets:

Quantile	Target	Actual (mean across datasets)
p10	10%	5.4%
p25	25%	19.1%
p50	50%	49.4%
p75	75%	79.9%
p90	90%	94.5%

Architecture

Raw Context (512 steps)
    → Instance Robust Scaler (median/IQR)
    → Adaptive Patching (patch_size=8)
    → Resolution Prefix Tuning (freq_id → 4 covariates)
    → Sequence Mixing Blocks × 8:
        ├── LongConv (global context, kernel=65)
        ├── DeltaNet RNN (local streaming, state_size=64)
        ├── Gated Router (learned blend)
        └── GatedMLP (expansion=2)
    → Multi-Task Heads:
        ├── Point Forecast (d_model → 1)
        ├── Monotonic Quantiles (p10–p90, 5 quantiles)
        ├── Context Reconstruction (anomaly detection)
        └── Trend / Seasonal Decomposition (3 components)

Component	Detail
Parameters	6,518,104 (~6.5M)
Context length	512 timesteps
Prediction length	48 steps (configurable)
Patch size	8
Hidden dim / layers	96 / 8
Quantiles	p10, p25, p50, p75, p90
Streaming	Stateful DeltaNet RNN — feed one value at a time
Deployment	ONNX (FP32 + INT8), FastAPI, Docker, Raspberry Pi, Browser

Deployment Options

FastAPI Server

bash

pip install nanoforecast fastapi uvicorn python-multipart
python3 deploy/fastapi_server.py
# → http://localhost:8000/docs

Docker

bash

docker build -t nanoforecast -f deploy/Dockerfile .
docker run -p 8000:8000 nanoforecast

ONNX (Edge / IoT / Browser)

bash

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

Inference Latency

NanoForecast runs 19.5ms on CPU (PyTorch) and 10.7ms via ONNX — no GPU required.

Live Gradio Demo

Upload a CSV → get a forecast + prediction intervals + decomposition plot. No code required.

Quick Start

Install

pip install nanoforecast

Zero-Shot Forecasting

python

import numpy as np
from nanoforecast import NanoForecast

model = NanoForecast.from_pretrained("eulogik/nanoforecast-v05")

# Generate context (or load your own time series)
context = np.sin(np.linspace(0, 8*np.pi, 512)) + 0.1 * np.random.randn(512)

# Forecast
result = model.predict(context, horizon=48, freq=1)

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

Streaming / Online Inference (unique to NanoForecast)

python

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

# Stream new observations one at a time
for new_val in incoming_stream:
    result = model.predict_step(new_val, state, horizon=48)
    forecast = result["forecast"][0]  # updated forecast instantly

From Your Own CSV

python3 train_from_csv.py --csv sales.csv --target revenue --horizon 48

How Does It Compare?

Feature	NanoForecast v0.5	TimesFM	Chronos-T5	Lag-Llama	PatchTST
Parameters	6.5M	200M	8M–710M	16.6M	15M+
CPU inference	19.5ms	GPU required	GPU required	GPU required	GPU required
Streaming	✅	❌	❌	❌	❌
ONNX export	✅	❌	❌	❌	❌
Raspberry Pi	✅	❌	❌	❌	❌
Quantiles	✅ (5)	⚠️	✅	✅	❌
Train from CSV	✅	❌	❌	⚠️	⚠️
License	Apache 2.0	Apache 2.0	Apache 2.0	Apache 2.0	Apache 2.0
Zero-shot	✅	✅	✅	✅	❌

When to Use NanoForecast

✅ Use when:

Deploying to edge/IoT devices (Raspberry Pi, ARM, browser)
Streaming/online inference (feed one value at a time)
Quantile forecasts with uncertainty estimates
Training on your own data in minutes
ONNX export for browser/ARM deployment
Apache 2.0 license required

❌ Don't use when:

You need SOTA accuracy on all benchmarks (use TimesFM, Chronos)
You have massive datasets (100K+ rows) — fine-tune a larger model
You need multivariate cross-series dependencies

Training

Reproduce on Colab (free T4 GPU, ~12h)

Parameter	Value
Datasets	ETTh1, ETTh2, ETTm1, exchange_rate, electricity, traffic
Synthetic records	10,000
Epochs	200 (best at 51)
Learning rate	3e-5 (OneCycleLR, peak 3e-4)
Batch size	128
Loss	MultiTaskLoss (point + quantile + anomaly + smooth)
Wall time	~12h on Colab T4

Model Files

File	Size
`model.safetensors`	26.1 MB
`config.json`	343 B
`model_card.json`	710 B
`standard_benchmark.json`	3.1 KB

Citation

bibtex

@article{nanoforecast2026,
  title={NanoForecast: A Deployable Time Series Foundation Model},
  author={Gautam Kishore and Eulogik},
  year={2026},
  url={https://github.com/eulogik/NanoForecast},
  note={6.5M parameters, CPU inference, ONNX export, streaming RNN}
}

eulogik/nanoforecast-v05

NanoForecast v0.5

6.5M-Parameter Time Series Foundation Model — Deploy Anywhere

What is NanoForecast?

Key Features

Benchmark Results

Parameter Efficiency

Head-to-Head Wins

Training-Pipeline Refinement: v0.3 → v0.5

Quantile Calibration

Architecture

Deployment Options

FastAPI Server

Docker

ONNX (Edge / IoT / Browser)

Inference Latency

Live Gradio Demo

Quick Start

Install

Zero-Shot Forecasting

Streaming / Online Inference (unique to NanoForecast)

From Your Own CSV

How Does It Compare?

When to Use NanoForecast

Training

Reproduce on Colab (free T4 GPU, ~12h)

Model Files

Citation

Links

eulogik/
nanoforecast-v05