DilationMappingTransformer
Dilation mapping transformer.
A transformer for applying an index grid dilation mapping to time series data, in the terminology of [1].
This transformation is motivated by kernel dilation, it reorders the timesteps of a time series to simulate the effect of dilation. For instance, in a pipeline, it enables a dilation-like effect for downstream models that do not inherently support such a feature.
Mathematically, the mapping operates on sequences \(x_1, \dots, x_k\). The dilation with factor \(d\) is defined as the sequence \(x_1, x_{1+d}, x_{1+2d}, \dots, x_2, x_{2+d}, x_{2+2d}, \dots, x_d, x_{2d}, \dots\), where the subsequences with grid spacing \(d\) are maximal.
The resulting sequence is of equal length to the input sequence.
This transformer reorders the values, and resets the sequence index to a RangeIndex, if the mtype is pandas based.
Schnellstart
from sktime.transformations.dilation_mapping import DilationMappingTransformer
estimator = DilationMappingTransformer(dilation=2)Tags
Fähigkeiten
- Fehlende Werte
- Reihen ungleicher Länge
- Multivariat: Nicht unterstützt
- Inverse Transformation: Nicht unterstützt
- Entfernt fehlende Werte: Nicht unterstützt
- Gleicht Reihenlängen an: Nicht unterstützt
Eigenschaften
- Eingabetypscitype:transform-input
- Series
- Ausgabetypscitype:transform-output
- Series
- Label-Typscitype:transform-labels
- None
- Fit ist leerfit_is_empty
- Ja
- Behält den Zeitindextransform-returns-same-time-index
- Nein
- Benötigt Xrequires_X
- Ja
- Benötigt yrequires_y
- Nein
- X und y brauchen denselben IndexX-y-must-have-same-index
- Nein
Parameter(1)
- dilationint, default=2
- The dilation factor. Determines the spacing between original data points in the transformed series. Must be an integer greater than 0. A dilation of 1 means no change, while higher values increase the spacing.
Beispiele
>>> from sktime.transformations.dilation_mapping import \
... DilationMappingTransformer
>>> from sktime.datasets import load_airline
>>> y = load_airline ()
>>> y_transform = DilationMappingTransformer (dilation = 2). fit_transform (y)Referenzen
Patrick Schäfer and Ulf Leser, “WEASEL 2.0–A Random Dilated Dictionary Transform for Fast, Accurate and Memory Constrained Time Series Classification”, 2023, arXiv preprint arXiv:2301.10194.