TimeSeriesHandler Guide

TimeSeriesHandler provides a configurable preprocessing pipeline for multivariate time series, including optional auto-configuration (self_tune=True).

Related docs:

Import

python

from foreblocks import TimeSeriesHandler

Return values from fit_transform(...):

X: input windows
y: forecast targets
processed: transformed full series
time_feats: optional time features (or None)

Core constructor options

Windowing

window_size (default 24)
horizon (default 10)

Main transforms

normalize (True)
differencing (False)
detrend (False)
log_transform (False)
apply_filter (False)
apply_ewt (False)

Outliers and imputation

remove_outliers (False)
outlier_method (default "iqr")
outlier_threshold (default 0.05)
apply_imputation (False)
impute_method (default "auto")

Auto-configuration / UX

self_tune (False)
generate_time_features (False)
plot (False)
verbose (False)

Typical workflow

python

# Fit on train data only
X_train, y_train, train_processed, tf_train = pre.fit_transform(train_data, time_stamps=train_ts)

# Transform validation/test using fitted state
X_val = pre.transform(val_data, time_stamps=val_ts)
X_test = pre.transform(test_data, time_stamps=test_ts)

# Invert model outputs back to data scale
pred_real = pre.inverse_transform(pred_scaled)

Operational notes

Input must be 2D: [T, F].
Call fit_transform before transform; otherwise transform raises.
transform returns windows X (not (X, y, processed)).
inverse_transform accepts shape [N, D] or [N, H, D].
Auto-configuration prints detected settings when enabled.

When to enable `self_tune`

Use self_tune=True for heterogeneous datasets or quick baselines.
Use manual configuration for controlled ablations/reproducibility studies.

TimeSeriesHandler Guide ​

Import ​

Core constructor options ​

Windowing ​

Main transforms ​

Outliers and imputation ​

Auto-configuration / UX ​

Typical workflow ​

Operational notes ​

Related pages ​

When to enable self_tune ​