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.