Enhancing Efficiency and Accuracy of Optimization Techniques in Time Series Data Prediction Using Machine Learning: A Systematic Literature Review

Abstract

Undoubtedly, time series data prediction stands as a primary focus of computational intelligence among researchers in both academia and industry, owing to its wide-ranging applications and significant impact. The efficacy of prediction models heavily relies on the optimization techniques employed to enhance both efficiency and accuracy. Within the realm of Machine Learning (ML), researchers have developed numerous optimization techniques and models, leading to a plethora of studies. Consequently, there exists a considerable body of literature comprising reviews of ML-based time series prediction. Recently, Deep Learning (DL) models have emerged in this domain, demonstrating performance levels that notably surpass those of traditional ML methods. Despite the burgeoning interest in advancing time series prediction models, there remains a noticeable absence of systematic review papers dedicated solely to enhancing the efficiency and accuracy of optimization techniques. Therefore, this paper is motivated by the need to present a systematic review of the efficiency and accuracy of optimizers studies concerning time series prediction implementations. We not only classify these studies based on their targeted optimization techniques, prediction applications—such as crop yield prediction, weather forecasting for farming, and pest detection and management—but also categorize them according to the types of optimization techniques and models employed, including Convolutional Neural Networks (CNNs), Deep Belief Networks (DBNs), and Long-Short Term Memory (LSTM) networks. Additionally, this study endeavor to provide insights into the future of the field by highlighting the challenges and potential future research opportunities, thereby offering guidance to interested researchers.