functime 0.1.5

The easiest way to run and deploy time-series ML (forecasting and classification) in the Cloud.

Run and scale time-series machine learning in the Cloud

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functime is a powerful and easy-to-use Cloud service for AutoML forecasting and time-series embeddings. The functime Python package provides a scikit-learn API and command-line interface to interact with functime Cloud.

Want to use functime for seamless time-series analytics across your data team? Looking for fully-managed production-grade AI/ML forecasting and time-series search? Book a 15 minute discovery call to learn more about functime's Team / Enterprise plans.

Highlights

• Fast: Forecast 100,000 time series in seconds on your laptop
• Efficient: Embarrassingly parallel feature engineering for time-series using Polars
• Battle-tested: Automated machine learning algorithms that deliver real business impact and win competitions
• Every forecaster supports exogenous features
• Backtesting with expanding window and sliding window splitters
• Automated lags and hyperparameter tuning using FLAML
• Utilities to add calendar effects, special events (e.g. holidays), weather patterns, and economic trends
• Supports recursive, direct, and ensemble forecast strategies

View detailed list of features including forecasters, preprocessors, feature extractors, and time-series splitters.

Getting Started

1. First, install functime via the pip package manager.

pip install functime

1. Then sign-up for a free functime Cloud account via the command-line interface (CLI).

functime login

1. That's it! You can begin forecasting at scale using the scikit-learn fit-predict interface.

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://bit.ly/commodities-data")
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)

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

All predictions and scores are returned as Polars DataFrames.

>>> y_pred
shape: (213, 3)
┌────────────────┬─────────────────────┬─────────────┐
│ commodity_type ┆ time                ┆ price       │
│ ---            ┆ ---                 ┆ ---         │
│ str            ┆ datetime[ns]        ┆ f64         │
╞════════════════╪═════════════════════╪═════════════╡
│ Wheat, US HRW  ┆ 2023-01-01 00:00:00 ┆ 240.337497  │
│ Wheat, US HRW  ┆ 2023-02-01 00:00:00 ┆ 250.851552  │
│ Wheat, US HRW  ┆ 2023-03-01 00:00:00 ┆ 252.102028  │
│ Beef           ┆ 2023-01-01 00:00:00 ┆ 4.271976    │
│ …              ┆ …                   ┆ …           │
│ Coconut oil    ┆ 2023-03-01 00:00:00 ┆ 1140.930346 │
│ Copper         ┆ 2023-01-01 00:00:00 ┆ 7329.806663 │
│ Copper         ┆ 2023-02-01 00:00:00 ┆ 7484.565165 │
│ Copper         ┆ 2023-03-01 00:00:00 ┆ 7486.160195 │
└────────────────┴─────────────────────┴─────────────┘

>>> scores.sort("mase")
shape: (71, 2)
┌──────────────────────┬────────────┐
│ commodity_type       ┆ mase       │
│ ---                  ┆ ---        │
│ str                  ┆ f64        │
╞══════════════════════╪════════════╡
│ Rice, Viet Namese 5% ┆ 0.308148   │
│ Palm kernel oil      ┆ 0.554886   │
│ Coconut oil          ┆ 1.051424   │
│ Cocoa                ┆ 1.32211    │
│ …                    ┆ …          │
│ Sugar, US            ┆ 73.346233  │
│ Sugar, world         ┆ 81.304941  │
│ Phosphate rock       ┆ 85.936644  │
│ Sugar, EU            ┆ 170.319435 │
└──────────────────────┴────────────┘

Deployment

functime deploys and trains your forecasting models the moment you call any .fit method. Run the functime list CLI command to list all deployed models. To view data and forecasts usage, run the functime usage CLI command.

You can reuse a deployed model for predictions anywhere using the stub_id variable.

forecaster = LinearModel.from_deployment(stub_id)
y_pred = forecaster.predict(fh=3)

License

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