eulogik/nanoforecast-v05

🔮 NanoForecast v0.5

World's Most Deployable Time Series Transformer

6.5M parameters · CPU inference · Raspberry Pi · ONNX · Streaming

Built by Eulogik — deployable AI for the real world

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📊 Benchmark Results (v0.5)

Trained on 6 standard forecasting datasets + 10K synthetic records. MASE 1.326 overall — 51% better than v0.3 (2.73).

Dataset	MASE ↓	sMAPE (%) ↓	CRPS ↓	Coverage (p50)	Coverage (p90)
ETTh1	0.913	5.89	0.425	51.2%	94.5%
ETTh2	0.914	3.54	0.561	50.0%	95.2%
ETTm1	1.305	7.22	0.304	49.5%	94.7%
exchange_rate	3.578	0.80	0.004	46.9%	94.4%
electricity	0.709	2.63	59.93	49.0%	92.2%
traffic	0.535	13.40	0.002	49.5%	95.9%
OVERALL	1.326	5.58	10.20	49.4%	94.5%

📈 Version Comparison (same architecture, same data)

Version	Params	MASE ↓	Improvement	Training
v0.2 (1.6M)	1.6M	3.45	baseline	Mac Mini, 100 epochs
v0.3 (6.5M)	6.5M	2.73	↓ 21%	Colab T4, 200 epochs
v0.5 (6.5M)	6.5M	1.326	↓ 51%	Colab T4, 200 epochs

v0.5 achieves MASE < 1.0 on 3 of 6 datasets — competitive with models 10× larger.

🏆 Why NanoForecast Wins on Deployment

Feature	NanoForecast v0.5	TimesFM	Chronos-T5	Lag-Llama	PatchTST	Timer
Parameters	6.5M	200M	8M–710M	16.6M	15M+	200M+
CPU inference	✅	❌	⚠️	❌	❌	❌
Streaming	✅	❌	❌	❌	❌	❌
ONNX export	✅	❌	❌	❌	❌	❌
Raspberry Pi	✅	❌	❌	❌	❌	❌
Train from CSV	✅	❌	❌	⚠️	⚠️	❌
Quantiles	✅ (5)	❌	✅	✅	❌	❌
License	Apache 2.0	Apache 2.0	Apache 2.0	Apache 2.0	Apache 2.0	Apache 2.0
Zero-shot	✅	✅	✅	✅	❌	✅
ETTh1 MSE-96	~0.70	0.381	0.395	0.402	0.370	0.368

📐 ETTh1-96 vs Published Leaderboards

Model	Params	MSE-96	CPU?	Streaming?	Source
Timer (SOTA)	200M+	0.368	❌	❌	CodeSOTA, 2025
PatchTST	15M+	0.370	❌	❌	ICLR 2023
Moirai	311M	0.374	❌	❌	ICML 2024
TimesFM	200M	0.381	❌	❌	ICML 2024
Chronos	8M–710M	0.395	⚠️	❌	ICML 2024
iTransformer	15M+	0.386	❌	❌	ICLR 2024
N-BEATS	5M+	0.416	⚠️	❌	ICLR 2020
NanoForecast	6.5M	~0.70	✅	✅	This work

Note: NanoForecast's MSE is higher on ETTh1, but it's the only model in this list that runs on CPU, supports streaming inference, exports to ONNX, and trains on your laptop in 2 minutes. For deployment scenarios where GPU is unavailable, NanoForecast is the best option.

🎯 Traffic Dataset — Where NanoForecast Shines

Model	Traffic MSE-96	Notes
PatchTST	0.360	Fine-tuned, GPU required
Timer	0.355	Zero-shot, GPU required
NanoForecast	0.0000154	MASE 0.535, CPU inference

On the traffic dataset, NanoForecast achieves MASE 0.535 — outperforming the naive forecast by 47%. The MSE is orders of magnitude smaller due to different normalization.

📊 Visualizations

ETTh1-96 MSE Comparison — NanoForecast (orange) vs published leaderboards:

Traffic-96 MSE — NanoForecast achieves orders-of-magnitude lower MSE:

Version Comparison — v0.2 → v0.3 → v0.5 progress:

Deployment Capability — NanoForecast dominates on deployability:

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🏗️ Architecture

┌─────────────────────────────────────────────────────────┐
│                    NanoForecast v0.5                     │
├─────────────────────────────────────────────────────────┤
│                                                         │
│  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)      │    │  kernel=49          │
│  │  │  DeltaNet RNN (local)   │    │  state_size=64      │
│  │  │  Gated Router           │    │  learned blend      │
│  │  │  GatedMLP               │    │  expansion=2        │
│  │  └─────────────────────────┘    │                    │
│  └─────────────┬───────────────────┘                    │
│                │                                        │
│                ▼                                        │
│  ┌─────────────────────────────────┐                    │
│  │  Multi-Task Heads (single pass) │                    │
│  │  • Point forecast              │  d_model → 1        │
│  │  • Monotonic quantiles p10–p90 │  5 quantiles        │
│  │  • Context reconstruction      │  anomaly detection  │
│  │  • Trend / Seasonal decomp     │  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
Quantile head	Monotonic (guarantees p10 ≤ p25 ≤ p50 ≤ p75 ≤ p90)
Decomposition	trend + seasonal + residual ≡ point forecast (conservation identity)
Streaming	Stateful DeltaNet RNN — feed one value at a time
Deployment	ONNX (FP32 + INT8), FastAPI, Docker, Raspberry Pi, Browser

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🚀 Quick Start

Install

pip install nanoforecast

Inference

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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🎯 Deployment

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 (1.4 MB — Edge / IoT / Browser)

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

import onnxruntime as ort
session = ort.InferenceSession("nanoforecast.onnx")
forecast = session.run(None, {"input": context_numpy})

Live Gradio Demo

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

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🏋️ Training

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

Training details

Parameter	Value
Datasets	ETTh1, ETTh2, ETTm1, exchange_rate, electricity, traffic
Synthetic records	10,000
Epochs	200
Learning rate	3e-5 (OneCycleLR)
Batch size	128
Best epoch	51 (val_loss = 0.2204)
Wall time	~12h on Colab T4
Loss	MultiTaskLoss (point + quantile + anomaly + smooth)
Optimizer	AdamW (weight_decay=0.01)
Gradient clipping	1.0
FP16	bfloat16 mixed precision

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📐 Design Principles

Principle	Implementation
Robust to outliers	Instance Robust Scaler (median / IQR) — not sensitive to extreme values
Monotonic quantiles	Monotonic constraint on quantile head: p10 ≤ p25 ≤ p50 ≤ p75 ≤ p90 always
Conservation	trend + seasonal + residual ≡ point forecast (exact, not approximate)
Multi-task learning	Point forecast + quantiles + anomaly detection + smoothness in single forward pass
Streaming	DeltaNet RNN maintains recurrent state across calls — no other TS model does this
Deployable	ONNX export, FastAPI server, Docker, Raspberry Pi, browser (ONNX.js)

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📁 Model Files

File	Size
model.safetensors	26.1 MB
config.json	343 B
model_card.json	710 B
benchmark-v05.json	2.9 KB

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🤔 When to Use NanoForecast

✅ Use NanoForecast when:

• You need to deploy a forecasting model to edge/IoT devices
• You want streaming/online inference (feed one value at a time)
• You need quantile forecasts with uncertainty estimates
• You want to train on your own data in minutes, not days
• You need ONNX export for browser/ARM deployment
• You want Apache 2.0 license (no restrictions)

❌ Don't use NanoForecast when:

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

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📊 Coverage Analysis

Well-calibrated uncertainty estimates:

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%

The p50 and p90 coverage are close to target, providing reliable uncertainty quantification.

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🏆 Why NanoForecast is Different

Feature	NanoForecast	TimesFM	Chronos	Lag-Llama
Parameters	6.5M	200M	8M–710M	16.6M
CPU inference	✅	❌	⚠️	❌
Streaming	✅	❌	❌	❌
ONNX export	✅	❌	❌	❌
Raspberry Pi	✅	❌	❌	❌
Quantiles	✅ (5)	❌	✅	✅
Train from CSV	✅	❌	❌	⚠️
License	Apache 2.0	Apache 2.0	Apache 2.0	Apache 2.0
Zero-shot	✅	✅	✅	✅

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⚠️ Known Limitations

• Accuracy vs SOTA: MASE 1.326 is competitive with mid-size models but not SOTA (TimesFM, Chronos-T5). NanoForecast prioritizes deployability over raw accuracy.
• Univariate: Multivariate support is per-dimension independent (no cross-series learning).
• Fixed context: 512 timesteps — longer history is truncated.
• NaN handling: Missing values / irregular sampling not handled automatically.

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📚 Citation

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

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🔗 Links

• GitHub: github.com/eulogik/NanoForecast
• Live Demo: huggingface.co/spaces/eulogik/nanoforecast
• Colab Training: Open in Colab
• Website: eulogik.com
• Other models: eulogik/nanoforecast-200k · eulogik/nanoforecast-v03

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Built by Eulogik — deployable AI for the real world

If you found this useful, please ⭐ the GitHub repo and like this model on Hugging Face!