Exploring Clustering Techniques for Time Series Analysis: A Case Study on Moroccan Stock Market Data
Volume 02 issue 02 page 01-14

This study examines the effectiveness of clustering techniques for analyzing time series data in the context of the Moroccan stock market, focusing on 50 companies. The research utilizes Euclidean and Dynamic Time Warping (DTW) clustering methodologies to endeavor to discern latent trends and patterns in the oscillations of stock values across diverse firms. The strategy consists of multiple crucial steps. The time series data is first preprocessed to ensure its consistency and dependability. Then, it is determined how many clusters would be most effective in grouping the data. The DTW distance, which identifies nonlinear similarities in time series data, and the Euclidean distance, which emphasizes similarities in pricing patterns, are then used to cluster the data. . ... read more .