Time Series Clustering: Unlocking Hidden Patterns Across Sectors
Time series clustering, an unsupervised learning technique, is gaining traction across various sectors. It groups data sequences collected over time based on their similarities, accounting for temporal dependencies and shifts in trend. Applications range from finance to healthcare, energy, and predictive maintenance.
Techniques like Dynamic Time Warping (DTW) align sequences by stretching or compressing time, making them less sensitive to time shifts. This robust similarity measure has proven useful in identifying similar stock price patterns, grouping patients with similar vital sign trends, and detecting early failure signs in machines.
Feature-based clustering transforms time series into statistical or frequency-domain features, then applies standard clustering algorithms. Correlation-based measures compare the shapes of time series, providing a different perspective on similarity. Shape-based clustering, on the other hand, directly compares the overall shape of time series to group similar patterns, focusing on structural similarity rather than raw values.
Euclidean Distance, a simple but sensitive similarity measure, calculates the straight-line distance between sequences. Model-based clustering assumes each time series is generated from a probabilistic model, such as Gaussian distributions or Hidden Markov Models, and clusters are represented as mixtures of probability distributions.
The objective of time series clustering is to identify hidden structures and patterns in temporal data for effective analysis and decision-making. Its applications span finance, healthcare, energy, climate and weather analysis, industrial IoT and predictive maintenance, and retail and e-commerce. Despite its potential, there's limited information about the participants in the 2024 time series analysis challenge based on the Data Science Blogathon 2024.
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