| title | TimeSeriesHandler Guide |
|---|---|
| description | Raw series preprocessing, windowing, scaling, imputation, and filtering. |
| editLink | true |
[[toc]]
TimeSeriesHandler provides a configurable preprocessing pipeline for multivariate time series, including optional auto-configuration (self_tune=True).
Related docs:
from foreblocks import TimeSeriesHandler
```python
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)
```toml
---
## 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.