functime 0.8.1

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 engineering.

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 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
• Censored forecaster: for zero-inflated and thresholding forecasts

Additional Highlights

functime comes with a specialized LLM agent to analyze, describe, and compare your forecasts! Check out the walkthrough here. Currently supports OpenAI's GPT-3.5 and GPT-4 as the underyling engine.

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/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
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.