functime is a powerful Python library for production-ready AutoML forecasting and temporal embeddings.

functime also comes with time-series preprocessing (box-cox, differencing etc), cross-validation splitters (expanding and sliding window), and forecast metrics (MASE, SMAPE etc). All optimized as lazy Polars transforms.

Want to use functime for seamless time-series predictive analytics across your data team? Looking for production-grade time-series AutoML in a serverless Cloud deployment? Shoot Chris a message on LinkedIn to learn more about functime Cloud.

• Fast: Forecast 100,000 time series in seconds on your laptop
• Efficient: Embarrassingly parallel feature engineering for time-series using Polars
• Battle-tested: Machine learning algorithms that deliver real business impact and win competitions
• Exogenous features: supported by every forecaster
• Backtesting with expanding window and sliding window splitters
• AutoML: Automated lags and hyperparameter tuning using FLAML
• Censored model: for zero-inflated and thresholding forecasts

Install functime via the pip package manager.

pip install functime

import polars as pl
from functime.cross_validation import train_test_split
from functime.forecasting import lightgbm
from functime.metrics import mase

# Load example data
y = pl.read_parquet("https://github.com/descendant-ai/functime/raw/main/data/commodities.parquet")
entity_col, time_col = y.columns[:2]

# Time series split
y_train, y_test = y.pipe(train_test_split(test_size=3))

# Fit-predict
model = lightgbm(freq="1mo", lags=24, max_horizons=3, strategy="ensemble")
model.fit(y=y_train)
y_pred = model.predict(fh=3)

# functime ❤️ functional design
# fit-predict in a single line
y_pred = lightgbm(freq="1mo", lags=24)(y=y_train, fh=3)

# Score forecasts in parallel
scores = mase(y_true=y_test, y_pred=y_pred, y_train=y_train)

Currently in closed-beta for functime Teams. Contact us for a demo via Calendly.

Deploy and train forecasters the moment you call any .fit method. Run the functime list CLI command to list all deployed models. Finally, track data and forecasts usage using functime usage CLI command.

You can reuse a deployed model for predictions anywhere using the stub_id variable. Note: the .from_deployed model class must be the same as during .fit.

forecaster = LightGBM.from_deployed(stub_id)
y_pred = forecaster.predict(fh=3)

functime is distributed under AGPL-3.0-only. For Apache-2.0 exceptions, see LICENSING.md.