eulogik/nanoforecast-v05

NanoForecast v0.5

6.5M-Parameter Time Series Foundation Model — Deploy Anywhere

CPU inference · ONNX · Streaming · Quantile forecasts · Edge/ARM

Built by Eulogik — deployable AI for the real world

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What is NanoForecast?

NanoForecast is a 6.5M-parameter time series foundation model that runs inference on CPUs and, via ONNX, on edge/ARM 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.

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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 (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

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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	0.676	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 26x better parameter efficiency (MASE$^{-1}$ per parameter) than TimesFM and is 2x more efficient than PatchTST.

Head-to-Head Wins

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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

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Quantile Coverage

Measured under the standard protocol (benchmark_standard.py; empirical P(target ≤ predicted quantile), mean across the six datasets):

Quantile	Target	Measured
p10	10%	20.1%
p25	25%	30.8%
p50	50%	45.4%
p75	75%	59.5%
p90	90%	71.4%

Honest note: the predicted intervals are narrower than nominal under this protocol — the p10–p90 band covers 51% of held-out values (target 80%). Quantiles are best read as relative uncertainty signals (e.g., ranking steps by uncertainty) rather than calibrated probabilities. Point forecasts (p50) are unaffected and remain the recommended output for accuracy.

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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, Browser

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Deployment Options

FastAPI Server

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

Docker

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

ONNX (Edge / IoT / Browser)

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.

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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-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)

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

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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	✅	❌	❌	❌	❌
Edge/ARM via ONNX	✅	❌	❌	❌	❌
Quantiles	✅ (5)	⚠️	✅	✅	❌
Train from CSV	✅	❌	❌	⚠️	⚠️
License	Apache 2.0	Apache 2.0	Apache 2.0	Apache 2.0	Apache 2.0
Zero-shot	✅	✅	✅	✅	❌

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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

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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

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Model Files

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

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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
• PyPI: pypi.org/project/nanoforecast
• Colab Training: Open in Colab
• Website: eulogik.com
• Other models: eulogik/nanoforecast-v03 · eulogik/nanoforecast-patchtst-baselines · eulogik/nanoforecast-200k

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Built by Eulogik — deployable AI for the real world

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