functime 0.8.4

Time-series machine learning at scale.

Time-series machine learning at scale

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functime is a powerful Python library for production-ready global forecasting and time-series feature extraction on large panel datasets.

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.

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Highlights

• Fast: Forecast and extract features (e.g. tsfresh, Catch22) across 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
• Automated lags and hyperparameter tuning using FLAML

Additional Highlights

functime comes with a specialized LLM agent to analyze, describe, and compare your forecasts. Check out the walkthrough here.

Getting Started

Install functime via the pip package manager.

pip install functime

functime comes with extra options. For example, to install functime with large-language model (LLM) and lightgbm features:

pip install "functime[llm,lgb]"

• cat: To use catboost forecaster
• xgb: To use xgboost forecaster
• lgb: To use lightgbm forecaster
• llm: To use the LLM-powered forecast analyst

Forecasting

import polars as pl
from functime.cross_validation import train_test_split
from functime.feature_extraction import add_fourier_terms
from functime.forecasting import linear_model
from functime.preprocessing import scale
from functime.metrics import mase

# Load commodities price data
y = pl.read_parquet("https://github.com/neocortexdb/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
forecaster = linear_model(freq="1mo", lags=24)
forecaster.fit(y=y_train)
y_pred = forecaster.predict(fh=3)

# functime ❤️ functional design
# fit-predict in a single line
y_pred = linear_model(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)

# Forecast with target transforms and feature transforms
forecaster = linear_model(
    freq="1mo",
    lags=24,
    target_transform=scale(),
    feature_transform=add_fourier_terms(sp=12, K=6)
)

# Forecast with exogenous regressors!
# Just pass them into X
X = (
    y.select([entity_col, time_col])
    .pipe(add_fourier_terms(sp=12, K=6)).collect()
)
X_train, X_future = y.pipe(train_test_split(test_size=3))
forecaster = linear_model(freq="1mo", lags=24)
forecaster.fit(y=y_train, X=X_train)
y_pred = forecaster.predict(fh=3, X=X_future)

View the full walkthrough on forecasting here.

LLM Copilot

Requires an OpenAI API key set as an environment variable OPENAI_API_KEY.

import polars as pl
import functime.llm

y = pl.read_parquet("../../data/commodities.parquet")
context = "This dataset comprises of historical commodity prices between 1980 to 2022."

# Analyze trend and seasonality for two commodities
analysis = y_pred.llm.analyze(
    context=dataset_context,
    basket=["Aluminum", "Banana, Europe"]
)
print("📊 Analysis:\n", analysis)

# Compare two baskets of commodities!
basket_a = ["Aluminum", "Banana, Europe"]
basket_b = ["Chicken", "Cocoa"]
comparison = y_pred.llm.compare(
    basket=basket_a,
    other_basket=basket_b
)
print("📊 Comparison:\n", comparison)

View the full walkthrough on the LLM copilot here.

License

functime is distributed under Apache-2.0.