sktime.datasets.base.load_arrow_head

sktime.datasets.base.load_arrow_head(split=None, return_X_y=False)[source]

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

Returns

  • X (pandas 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

  • Details

  • ——-

  • 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