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