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

Abstract

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 is how to reduce the search space (state and observation space). To address this issue, we propose a graph-based big data optimization approach using a CSP to enhance the results of learning and prediction tasks of HMMs. This approach takes full advantage of both HMMs, with the richness of their algorithms, and CSPs, with their many powerful and efficient solver algorithms. To verify the validity of the model, the proposed approach is evaluated on real-world data using the mean absolute percentage error (MAPE) and other metrics as measures of the prediction accuracy. The conducted experiments show that the proposed model outperforms the conventional model. It reduces the MAPE by 0.71% and offers a particularly good trade-off between computational costs and the quality of results for large datasets. It is also competitive with benchmark models in terms of the running time and prediction accuracy. Further comparisons substantiate these experimental findings.

Topological Variable Neighborhood Search

“…In the field of machine learning, optimization algorithms are widely used to analyze large volumes of data and calculate parameters of models used for predictions or classifications [7]. Thanks to the high efficiency of optimization solutions and the variety of applications that can be formulated as an optimization problem, optimization plays an important role in the development of new approaches to solving machine learning problems [8].…”

Stock Selection through Hidden Markov Model: A Case of Pakistan Stock Exchange

“…However, one challenge in employing HMMs to forecast time series is how to condense the search space (state and observation space). To improve the performance of learning and prediction tasks for HMMs, we suggest a graph-based large data optimization strategy employing a CSP (Sassi et al, 2021). In order to distinguish between several market regimes on the US stock market, Wang et al (2020) employ a hidden Markov model (HMM).…”