tslearn

import numpy as np from tslearn.clustering import TimeSeriesKMeans from tslearn.utils import to_time_series_dataset # Generate some sample time series data np.random.seed(0) n_ts = 10 # Number of time series max_sz = 100 # Maximum length of time series d = 1 # Dimensionality of each time point (univariate) # Create a list of 2D numpy arrays for variable-length time series my_time_series = [] for i in range(n_ts): length = np.random.randint(50, max_sz + 1) series = np.random.rand(length, d) my_time_series.append(series) # Convert to tslearn's expected 3D dataset format X = to_time_series_dataset(my_time_series) # Initialize and fit a TimeSeriesKMeans model # Using dtw (Dynamic Time Warping) as the metric km = TimeSeriesKMeans(n_clusters=2, metric="dtw", max_iter=10, random_state=0) cluster_labels = km.fit_predict(X) print(f"Input shape: {X.shape}") print(f"Cluster labels: {cluster_labels}")