API Reference / preprocessing / timeseries /

lag.py

Time series lag (translate) transformer.

Creates lagged (shifted) features from time series data.

Classes

LagTransformer

class preprocessing.timeseries.lag.LagTransformer(Transformer)

Create lagged features from time-ordered data.

Shifts feature values by a specified number of periods. Useful for transforming time series data into a supervised learning format where past values are used to predict future ones.
Constructor 
__init__(
    self,
    lag: int = ...,
    columns: list[int] | None = None,
    fill_with_missing: bool = True,
    invert_selection: bool = False,
)

Parameters

lag
int = -1

The number of periods to shift.

  • •Negative values (e.g., -1): Use past values (most common).
  • •Positive values (e.g., 1): Use future values (look-ahead).
columns
list of int
Indices of columns to shift. If None, all columns are lagged.
fill_with_missing
bool = True
  • •If True: Keeps the original number of rows and fills boundary

indices with np.nan.

  • •If False: Removes the rows that would contain np.nan results.
invert_selection
bool = False
If True, applies the lag to all columns except those specified in columns.

Attributes

feature_names_out_
list of str
The generated names for the shifted features (e.g., "x-1").

See Also

DifferenceTransformer

Computes the change between periods.

Lag a feature by 1 period to use the previous day's value:

python 
>>> from tuiml.preprocessing.timeseries import LagTransformer
>>> import numpy as np
>>> X = np.array([[10], [20], [30], [40]])
>>> lagger = LagTransformer(lag=-1)
>>> X_lagged = lagger.fit_transform(X)
>>> print(X_lagged.flatten())
[nan 10. 20. 30.]

Methods

get_parameter_schema (cls)
fit (self, X: np.ndarray, y: np.ndarray | None=None, feature_names: list[str] | None=None) -> 'LagTransformer'

Fit the transformer.

Parameters
X
np.ndarray of shape (n_samples, n_features)
Input data. Only its shape is recorded; no statistics are learned.
y
np.ndarray
Ignored, present for API consistency.
feature_names
list of str
Names of the input columns, used to label the generated columns.
Returns
self
object
The fitted transformer.
transform (self, X: np.ndarray) -> np.ndarray

Transform the data into shifted values.

Parameters
X
np.ndarray of shape (n_samples, n_features)
Input data, assumed to be ordered in time.
Returns
X_out
np.ndarray
Transformed data holding the lagged values.
get_feature_names_out (self, input_features: list[str] | None=None) -> list[str]

Get output feature names.

__repr__ (self) -> str