Investigating temporal patterns using hidden Markov models with lag analysis

Analyzing temporal patterns in time series data is crucial for improving forecasting accuracy and understanding underlying trends. This study explores the use of Hidden Markov Models (HMMs) with lag integration to better capture sequential dependencies. By incorporating past observations, the proposed method aims to enhance predictive accuracy and state identification. The performance of the lag-integrated HMM is evaluated using historical agricultural data and compared against conventional HMMs. This research contributes to time series modeling by demonstrating the benefits of lag incorporation in capturing complex temporal dynamics. A trained HMM, implemented in MATLAB, generates future projections based on learned patterns and lag-based adjustments. The study further assesses the impact of increasing lag on predictive performance using divergence measures such as Kullback-Leibler (KL) divergence, Jensen-Shannon (JS) divergence, and Bhattacharyya distance. Findings indicate that as the lag increases, prediction accuracy declines. The results enhance agricultural forecasting methodologies, supporting efficient resource allocation and sustainable farm management.