Preprocessing
functime supports parallelized time-series preprocessing using Polars. All functime preprocessors take a panel DataFrame as a input and transform each time-series locally (i.e. time-series by time-series as a parallelized group_by operation).
Time-series transformations are commonly used to stabilize the time-series (e.g. boxcox for variance stabilzation) or make the time-series stationary through first differences or detrending. Some transformations are also invertible, such as diff and detrend, which is useful for converting the forecast of a transformed time-series back to the original scale.
Check out the API reference for details.
Quick Examples
Differencing
Apply k-order differences. This transform is invertible.
from functime.preprocessing import diff
transformer = diff(order=1)
X_new = X.pipe(transformer).collect()
X_original = transformer.invert(X_new)
Seasonal Differencing
Apply k-order differences shifted by sp periods. This transform is invertible.
from functime.preprocessing import diff
# Assume X is a monthly dataset with seasonal period = 12
transformer = diff(order=1, sp=12)
X_new = X.pipe(transformer).collect()
X_original = transformer.invert(X_new)
Detrending (Linear)
Removes linear trend for each time-series. This transform is invertible.
from functime.preprocessing import detrend
transformer = detrend(method="linear")
X_new = X.pipe(transformer).collect()
X_original = transformer.invert(X_new)
Detrending (Mean)
Removes mean trend for each time-series. This transform is invertible.
from functime.preprocessing import detrend
transformer = detrend(method="mean")
X_new = X.pipe(transformer).collect()
X_original = transformer.invert(X_new)
Box-Cox
Applies optimized Box-Cox transform for each time-series. This transform is invertible.
from functime.preprocessing import boxcox
transformer = boxcox(method="mle")
X_new = X.pipe(transformer).collect()
X_original = transformer.invert(X_new)
Yeo-Johnson
Applies optimized Yeo-Johnson transform for each time-series. This transform is invertible.
from functime.preprocessing import yeojohnson
transformer = yeojohnson()
X_new = X.pipe(transformer).collect()
X_original = transformer.invert(X_new)
Local Scaling
Standardizes each time-series with subtracting mean and dividing by the standard deviation. This transform is invertible.
from functime.preprocessing import scale
transformer = scale(use_mean=True, use_std=True)
X_new = X.pipe(transformer).collect()
X_original = transformer.invert(X_new)
Rolling Statistics
Given a list of window sizes, applies rolling statistics for each time-series across each column. This transform is not invertible. Currently supports the following statistics: mean, min, max, mlm (max less min), sum, std, cv (coefficient of variation).