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ts-arena/chronos_t5_tiny_8m_forecasting

sourceHugging Faceapache-2.0updated 9mo agoView on Hugging Face
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chronost5tiny8mforecasting

TS Arena wrapper for Amazon Chronos T5-Tiny time series forecasting model.

Model Description

Chronos is a family of pretrained time series forecasting models based on language model architectures. It tokenizes time series values using scaling and quantization, then uses a T5 model to generate probabilistic forecasts.

AttributeValue
Parameters8M
ArchitectureT5 Encoder-Decoder
Original Repoamazon/chronos-t5-tiny
PaperChronos: Learning the Language of Time Series
TaskTime Series Forecasting

Usage with TS Arena

python
import ts_arena

# Load model
model = ts_arena.load_model("chronos-t5-tiny")

# Generate forecasts
import numpy as np
context = np.random.randn(96)  # 96 timesteps of history
output = model.predict(context, prediction_length=24, num_samples=20)

# Access results
print(output.predictions.shape)      # Point forecasts (median)
print(output.quantiles[0.5].shape)   # Median forecast
print(output.quantiles[0.1].shape)   # 10th percentile
print(output.quantiles[0.9].shape)   # 90th percentile

Direct Usage with Chronos

python
from chronos import ChronosPipeline
import torch

pipeline = ChronosPipeline.from_pretrained(
    "amazon/chronos-t5-tiny",
    device_map="cuda",
    torch_dtype=torch.bfloat16,
)

context = torch.randn(1, 96)  # (batch, time)
forecast = pipeline.predict(context, prediction_length=24, num_samples=20)

Evaluation Results

ETTh1 Dataset (context=96, horizon=96)

MetricValue
MSE4.37
MAE1.66
RMSE2.09

Features

  • —Zero-shot forecasting: No training required
  • —Probabilistic forecasts: Returns samples and quantiles
  • —Variable horizons: Supports different prediction lengths
  • —Multivariate support: Processes each channel independently

Limitations

  • —Univariate model (multivariate handled channel-by-channel)
  • —No exogenous variable support
  • —Recommended max prediction length: 64

Citation

bibtex
@article{ansari2024chronos,
  title={Chronos: Learning the Language of Time Series},
  author={Ansari, Abdul Fatir and Stella, Lorenzo and Turkmen, Caner and Zhang, Xiyuan and others},
  journal={arXiv preprint arXiv:2403.07815},
  year={2024}
}

License

Apache-2.0 (following the original Chronos license)

Links