chronos_bolt_base_205m_forecasting

chronos_bolt_base_205m_forecasting

TS Arena wrapper for Amazon Chronos BOLT-Base time series forecasting model.

Model Description

Chronos-Bolt is an optimized version of the Chronos family, designed for faster inference while maintaining accuracy. It uses an optimized T5 architecture with improved tokenization and reduced computational overhead.

Usage with TS Arena

import ts_arena

# Load model
model = ts_arena.load_model("chronos-bolt-base")

# 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

from chronos import ChronosPipeline
import torch

pipeline = ChronosPipeline.from_pretrained(
    "amazon/chronos-bolt-base",
    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)

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

@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

• TS Arena GitHub
• Original Chronos
• Chronos Paper