load_arrow_head#

load_arrow_head(split=None, return_X_y=True, return_type='nested_univ')[source]#

Load the ArrowHead time series classification problem and returns X and y.

Parameters

split: None or str{“train”, “test”}, optional (default=None): Whether to load the train or test partition of the problem. By default it loads both.
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.

Returns

X: pd.DataFrame with m rows and c columns: The time series data for the problem with m cases and c dimensions
y: numpy array: The class labels for each case in X

Notes

Dimensionality: univariate Series length: 251 Train cases: 36 Test cases: 175 Number of classes: 3

The arrowhead data consists of outlines of the images of arrowheads. The shapes of the projectile points are converted into a time series using the angle-based method. The classification of projectile points is an important topic in anthropology. The classes are based on shape distinctions such as the presence and location of a notch in the arrow. The problem in the repository is a length normalised version of that used in Ye09shapelets. The three classes are called “Avonlea”, “Clovis” and “Mix”.”

Dataset details: http://timeseriesclassification.com/description.php ?Dataset=ArrowHead

Examples

>>> from sktime.datasets import load_arrow_head
>>> X, y = load_arrow_head()