functime-org/functime

Health v2 · maintenance only

Adoption not part of the score

1183 stars · 65 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

functime is a Python library for production-ready global forecasting and time-series feature extraction on large panel datasets, built on lazy Polars transforms for parallelism. It includes preprocessing, cross-validation splitters, forecast metrics, automated hyperparameter tuning, and an LLM agent for forecast analysis.

Use cases

• forecast 100,000 time series in seconds on a laptop
• extract tsfresh and Catch22 features from panel data in parallel
• backtest forecasts with expanding and sliding window splitters
• tune forecast hyperparameters and lags automatically with FLAML
• produce point and probabilistic forecasts with exogenous features
• preprocess time series with box-cox and differencing
• analyze and compare forecasts with an LLM agent

When to choose

• you need fast forecasting across many related time series (panel data)
• you want Polars-based parallel feature engineering for time series
• you need an end-to-end forecasting pipeline with backtesting and metrics
• you want ML-based forecasters with exogenous feature support

When to avoid

• you need classical statistical forecasting only (e.g. simple ARIMA on a single series)
• your stack is pandas-only and you cannot adopt Polars
• you need deep-learning forecasting models like N-BEATS or TFT

Facets

library · maturity active

machine-learning data-science etl machine-learning data-science time-series analytics python time-series forecasting polars feature-engineering panel-data backtesting global-forecasting

Member repositories

For agents

markdown · JSON · MCP: product_card(name="functime-org/functime")