title: Time Series Classification with WEASEL
body: <p>The Word ExtrAction for time SEries cLassification (WEASEL) algorithm extracts words from time series and builds features representing frequencies of each word for each time series. This algorithm is included in the <a href="https://pyts.readthedocs.io" rel="nofollow noreferrer">pyts</a> library. An example for visualizing the corresponding transform can be found <a href="https://pyts.readthedocs.io/en/stable/auto_examples/transformation/plot_weasel.html" rel="nofollow noreferrer">here</a> but I'm struggling to implement a working classifier. This code provides me with a transformed array of the training data:</p>
  <pre><code>from pyts.datasets import load_gunpoint
  from pyts.transformation import WEASEL
  
  # Toy dataset 
  X_train, _, y_train, _ = load_gunpoint(return_X_y=True)
  
  # WEASEL transformation
  weasel = WEASEL(word_size=2, n_bins=2, window_sizes=[12, 36], sparse=False)
  X_weasel = weasel.fit_transform(X_train, y_train)
  </code></pre>
  <p>But how to proceed from here? What approaches would I used to actually use these transformed data to perform a classification task? How would I feed the transformed testing data into this pipeline?</p>