Poly-linear regression with augmented long short term memory neural network: Predicting time series data

Abstract

Until recently, the supply chain sector, which had been getting by with scattered spreadsheets, phone conversations, and even paper-based records until recently, was exposed for its antiquated methods during the epidemic. As a result, businesses have undergone a decade of digital change in only a few months, with the epidemic driving them to replace antiquated procedures with AI, machine learning, and data science technology. The supply chain sector has reached a point in its AI adoption where the technology is solid and powerful enough to improve decision-making significantly. For example, predictive analytics (e.g., time series forecasting) is already a proven benefit. Such technology is smart enough to recognise irregularities and learn how a stock market will move in real-time. With the advancement of digital innovation, researchers have focused on deep learning (DL) models to get a more accurate and unbiased estimation. Consequently, this paper presents a novel DL approach for time series prediction using a combination of poly-linear regression with Long Short-Term Memory (LSTM) and data augmentation. It is consequently named Poly-linear Regression with Augmented Long Short Term Memory Neural Network (PLR-ALSTM-NN). The proposed DL model can be exploited to predict the future financial markets more accurately than existing state-of-the-art neural networks and machine learning tools. In order to make the model a more generic one, it is first validated on four financial market time-series datasets and then also implemented on a supply chain time-series dataset to predict sales data. LSTM, with its feedback connections, can process an entire series of data as well as single data points and statistical regression establishes the strength and character of the relationship between some dependent and independent variables. After doing experimental validations and based on the long-term and short-term predicted data, the suitability of the proposed PLR-ALSTM-NN is well-grounded against a few recent and advanced state-of-the-art machine learning, and DL approaches