Predict the Price in Stock Market Based on Heuristic Search Techniques Using Tensor Representation

Abstract

Our stock price forecast has a solid data foundation thanks to the abundance of indicators used in the financial sector to characterize changes in stock price. Due to their various industrial sectors and geographical locations, different stocks are impacted by various factors. Consequently, finding a multi-disciplinary team is crucial. To anticipate the price of a stock, choose a factor combination that is appropriate for that stock. In this paper, This approach, however, overlooks the interactions between multiple modes and combines different data modes into a single composite vector. The data’s heterogeneity in terms of sample period is the second problem. Fundamental data are made up of continuous values taken at regular intervals, whereas news information is generated at random. The feature spaces may get distorted or valuable information may be partially missing as a result of this heterogeneity.