multimodal-timesfm 0.1.0

Multimodal extension of Google's TimesFM for time series forecasting with text

Multimodal TimesFM

A multimodal extension of Google's TimesFM for time series forecasting with text inputs.

Installation

pip install multimodal-timesfm

Quick Start

from multimodal_timesfm import MultimodalTimesFM, TimesFmHparams, MultimodalTimesFMConfig

# Configure model
hparams = TimesFmHparams(context_len=512, horizon_len=128)
config = MultimodalTimesFMConfig(text_encoder_type="english")
model = MultimodalTimesFM(hparams, config, "checkpoint.pt")

# Forecast with time series and text descriptions
forecasts, quantiles = model.forecast(
    inputs=[time_series_data],
    text_descriptions=[[[["Market volatility high"]]]],
    freq=[0],
    forecast_context_len=128
)

Features

• Multimodal forecasting: Combines time series data with textual context
• Built on TimesFM: Leverages Google's state-of-the-art time series foundation model
• Flexible text encoding: Supports English and Japanese text inputs
• Easy integration: Simple API for adding text context to time series forecasting

Examples

See the examples/ directory for complete usage examples including training on the Time-MMD dataset.

Acknowledgments

We thank the Time-MMD team for providing the multimodal time series dataset used in our examples and experiments.

License

MIT