load_japanese_vowels(split=None, return_X_y=True, return_type='np-list')[source]#

Load the JapaneseVowels time series classification problem.

Example of a multivariate problem with unequal length series.

split: None or one of “TRAIN”, “TEST”, optional (default=None)

Whether to load the train or test instances of the problem. By default it loads both train and test instances into a single array.

return_X_y: bool, optional (default=True)

If True, returns (features, target) separately instead of a single dataframe with columns for features and the target.

return_type: string, default=”np-list”

Data structure to use for time series, should be “nested_univ” or “np-list”.

X: np.Pandas dataframe with 12 columns and a pd.Series in each cell
y: 1D numpy array of length n, only returned if return_X_y if True

The class labels for each time series instance in X If return_X_y is False, y is appended to X instead.


Dimensionality: 12 Series length: variable (7-29) Train cases: 270 Test cases: 370 Number of classes: 9 Details: http://timeseriesclassification.com/description.php?Dataset=JapaneseVowels


>>> from aeon.datasets import load_japanese_vowels
>>> X, y = load_japanese_vowels()