A graph-based big data optimization approach using hidden Markov model and constraint satisfaction problem

To address the challenges of big data analytics, several works have focused on big data optimization using metaheuristics. The constraint satisfaction problem (CSP) is a fundamental concept of metaheuristics that has shown great efficiency in several fields. Hidden Markov models (HMMs) are powerful machine learning algorithms that are applied especially frequently in time series analysis. However, one issue in forecasting time series using HMMs...