Time Series Analysis, Clustering, and Heuristic Algorithms with MATLAB

In this article, I will demonstrate how to perform time series analysis, clustering analysis, and implement heuristic algorithms using MATLAB. These methodologies are highly valuable in data analysis and pattern recognition applications. Time series analysis helps identify data trends and periodic patterns using functions like autocorrelation analysis, seasonal decomposition, and forecasting models (ARIMA, GARCH). Clustering analysis enables discovery of hidden patterns and natural groupings in datasets through algorithms such as k-means, hierarchical clustering, and DBSCAN, which can be implemented using MATLAB's Statistics and Machine Learning Toolbox. Heuristic algorithms provide approximate optimal solutions for complex optimization problems, including genetic algorithms, simulated annealing, and particle swarm optimization, accessible via MATLAB's Global Optimization Toolbox. My personally curated collection includes practical code examples demonstrating how to preprocess data using timetable arrays, perform clustering with pdist and cluster functions, and optimize parameters using heuristic approaches. These resources offer valuable insights for researchers and practitioners working with analytical methodologies.