Unsupervised Learning for Time Series Data

Duration: 7 min

This module delves into the application of unsupervised learning techniques for analyzing time series data. We will explore algorithms like K-Means, DBSCAN, Hierarchical Clustering, PCA, t-SNE, and Autoencoders, and understand how they can be effectively used to uncover patterns, reduce dimensionality, and cluster time series data without labeled outcomes.

K-Means Clustering for Time Series Data

K-Means clustering is a popular unsupervised learning algorithm used to partition data into distinct clusters based on feature similarity. In the context of time series data, K-Means can be used to group similar time series together, which can be useful for anomaly detection, pattern recognition, and data summarization.

import numpy as np
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler

# Generate synthetic time series data
np.random.seed(0)
data = np.random.rand(100, 10)

# Standardize the data
scaler = StandardScaler()
data_scaled = scaler.fit_transform(data)

# Apply K-Means clustering
kmeans = KMeans(n_clusters=3, random_state=0).fit(data_scaled)

# Get cluster labels
labels = kmeans.labels_
print(labels)

Try it in Google Colab:

[1 0 2... 0 2 1]

DBSCAN Clustering for Time Series Data

DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is another unsupervised learning algorithm that can identify clusters of varying shapes and sizes in time series data. Unlike K-Means, DBSCAN does not require specifying the number of clusters beforehand and can detect outliers effectively.

from sklearn.cluster import DBSCAN

# Apply DBSCAN clustering
dbscan = DBSCAN(eps=0.3, min_samples=5).fit(data_scaled)

# Get cluster labels
labels_dbscan = dbscan.labels_
print(labels_dbscan)

💡 Tip: When using DBSCAN, carefully choose the 'eps' and'min_samples' parameters to ensure meaningful clusters are formed.