tslearn.preprocessing
.TimeSeriesResampler¶
- class tslearn.preprocessing.TimeSeriesResampler(sz)[source]¶
Resampler for time series. Resample time series so that they reach the target size.
- Parameters:
- szint
Size of the output time series.
Examples
>>> TimeSeriesResampler(sz=5).fit_transform([[0, 3, 6]]) array([[[0. ], [1.5], [3. ], [4.5], [6. ]]])
Methods
fit
(X[, y])A dummy method such that it complies to the sklearn requirements.
fit_transform
(X[, y])Fit to data, then transform it.
set_output
(*[, transform])Set output container.
transform
(X[, y])Fit to data, then transform it.
- fit(X, y=None, **kwargs)[source]¶
A dummy method such that it complies to the sklearn requirements. Since this method is completely stateless, it just returns itself.
- Parameters:
- X
Ignored
- Returns:
- self
- fit_transform(X, y=None, **kwargs)[source]¶
Fit to data, then transform it.
- Parameters:
- Xarray-like of shape (n_ts, sz, d)
Time series dataset to be resampled.
- Returns:
- numpy.ndarray
Resampled time series dataset.
- set_output(*, transform=None)[source]¶
Set output container.
See Introducing the set_output API for an example on how to use the API.
- Parameters:
- transform{“default”, “pandas”}, default=None
Configure output of transform and fit_transform.
“default”: Default output format of a transformer
“pandas”: DataFrame output
None: Transform configuration is unchanged
- Returns:
- selfestimator instance
Estimator instance.