Classic machine-learning algorithms are fed with multiple observations, where each of them has a pre-defined size (that is, the feature size). While working with timeseries, we don't have a pre-defined length: we want to create something that works for both 10 days look-back, but also for three years look-back. How is this possible?
It's very simple, instead of varying the number of features, we will change the number of observations, maintaining a constant feature size. Each observation represents a temporal window of the timeseries, and by sliding the window of one position on the right we create another observation. In code:
def format_dataset(values, temporal_features):
feat_splits = [values[i:i + temporal_features] for i in range(len(values) - temporal_features)]
feats = np.vstack(feat_splits)
labels = np.array(values[temporal_features...
