Modeling and Decoding Hidden States in Sequential Data Using Hidden Markov Model
Vyshnavi M *
Department of Statistics, PSG College of Arts & Science, Coimbatore, India.
Muthukumar M
Department of Statistics, PSG College of Arts & Science, Coimbatore, India.
*Author to whom correspondence should be addressed.
Abstract
Aims: This study presents a comprehensive exploration of Hidden Markov Models (HMMs) for modeling and decoding hidden structures within sequential agricultural data, specifically the oilseed area from 1992 to 2022.
Study Design: HMMs with varying numbers of hidden states, ranging from two to eight, were constructed to analyze the underlying patterns in the time series.
Place and Duration of Study: The study was based on historical agricultural data collected from India, covering a period of 30 years, with model development and analysis conducted across different model state configurations ranging from two to eight.
Methodology: For each model configuration, key parameters, including the Transition Probability Matrix (TPM), Emission Probability Matrix (EPM), and initial state distribution (π), were estimated. Model performance was evaluated using standard selection criteria such as the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC) to determine the optimal number of states. The Viterbi algorithm was then employed to decode the most probable sequence of hidden states corresponding to the observed data.
Results: Results indicate that the model with two hidden states provides the best fit, effectively capturing the temporal dynamics of the oilseed area.
Conclusion: This work highlights the potential of Hidden Markov Models in uncovering latent structures in agricultural datasets and supports data-driven decision-making in crop planning and agricultural policy design.
Keywords: Hidden Markov Model, oilseed, Viterbi algorithm, Optimal state