发现神奇的 Python 库：functime

今天要向大家介绍一个神奇的 Python 库——functime！

链接：https://github.com/functime-org/functime

functime 是一个用于时间序列预测的机器学习库，可在大型数据集上进行预测和时间序列特征提取。其独特之处在于预处理选项和创新的交叉验证拆分器，具有无与伦比的速度和效率，可在几秒钟内处理数量惊人的 100,000 个时间序列。

特点：

• 功能齐全：提供强大且易于使用的 API，用于预测和特征工程
• 快速：在笔记本电脑上几秒钟内预测 100,000 个时间序列
• 高效：使用 Polars 进行并行特征工程
• 经过验证：能够产生真正的业务影响并赢得竞争的算法

案例展示：

使用 functime 进行时间序列预测的示例代码：

<code>import polars as pl from functime.cross_validation import train_test_split from functime.seasonality 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/TracecatHQ/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) # 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)</code>

特征提取示例：

<code>import polars as pl import numpy as np from functime.feature_extractors import FeatureExtractor, binned_entropy # Load commodities price data y = pl.read_parquet("https://github.com/TracecatHQ/functime/raw/main/data/commodities.parquet") # Get column names ("commodity_type", "time", "price") entity_col, time_col, value_col = y.columns # Extract a single feature from a single time-series binned_entropy = binned_entropy( pl.Series(np.random.normal(0, 1, size=10)), bin_count=10 ) # Also works on LazyFrames with query optimization features = ( pl.LazyFrame({ "index": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "value": np.random.normal(0, 1, size=10) }) .select( binned_entropy=pl.col("value").ts.binned_entropy(bin_count=10), lempel_ziv_complexity=pl.col("value").ts.lempel_ziv_complexity(threshold=3), longest_streak_above_mean=pl.col("value").ts.longest_streak_above_mean(), ) .collect() ) # Extract features blazingly fast on many # stacked time-series using `group_by` features = ( y.group_by(entity_col) .agg( binned_entropy=pl.col(value_col).ts.binned_entropy(bin_count=10), lempel_ziv_complexity=pl.col(value_col).ts.lempel_ziv_complexity(threshold=3), longest_streak_above_mean=pl.col(value_col).ts.longest_streak_above_mean(), ) ) # Extract features blazingly fast on windows # of many time-series using `group_by_dynamic` features = ( # Compute rolling features at yearly intervals y.group_by_dynamic( time_col, every="12mo", by=entity_col, ) .agg( binned_entropy=pl.col(value_col).ts.binned_entropy(bin_count=10), lempel_ziv_complexity=pl.col(value_col).ts.lempel_ziv_complexity(threshold=3), longest_streak_above_mean=pl.col(value_col).ts.longest_streak_above_mean(), ) )</code>

这些示例展示了 functime 的强大功能和简单易用的 API，帮助您在时间序列分析中取得更好的成果。