KShapeΒΆ

This example uses the KShape clustering method [1] that is based on cross-correlation to cluster time series.

[1] J. Paparrizos & L. Gravano. k-Shape: Efficient and Accurate Clustering of Time Series. SIGMOD 2015. pp. 1855-1870.

Cluster 1, Cluster 2, Cluster 3

Out:

0.008 --> 0.006 --> 0.004 --> 0.004 --> 0.004 --> 0.003 --> 0.003 --> 0.003 --> 0.003 --> 0.002 --> 0.002 --> 0.002 --> 0.002 --> 0.002 --> 0.002 --> 0.002 --> 0.002 --> 0.002 --> 0.002 --> 0.002 --> 0.002 --> 0.002 --> 0.002 --> 0.002 --> 0.002 --> 0.002 --> 0.002 -->

# Author: Romain Tavenard
# License: BSD 3 clause

import numpy
import matplotlib.pyplot as plt

from tslearn.clustering import KShape
from tslearn.datasets import CachedDatasets
from tslearn.preprocessing import TimeSeriesScalerMeanVariance

seed = 0
numpy.random.seed(seed)
X_train, y_train, X_test, y_test = CachedDatasets().load_dataset("Trace")
# Keep first 3 classes and 50 first time series
X_train = X_train[y_train < 4]
X_train = X_train[:50]
numpy.random.shuffle(X_train)
# For this method to operate properly, prior scaling is required
X_train = TimeSeriesScalerMeanVariance().fit_transform(X_train)
sz = X_train.shape[1]

# kShape clustering
ks = KShape(n_clusters=3, verbose=True, random_state=seed)
y_pred = ks.fit_predict(X_train)

plt.figure()
for yi in range(3):
    plt.subplot(3, 1, 1 + yi)
    for xx in X_train[y_pred == yi]:
        plt.plot(xx.ravel(), "k-", alpha=.2)
    plt.plot(ks.cluster_centers_[yi].ravel(), "r-")
    plt.xlim(0, sz)
    plt.ylim(-4, 4)
    plt.title("Cluster %d" % (yi + 1))

plt.tight_layout()
plt.show()

Total running time of the script: ( 0 minutes 2.495 seconds)

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