AGGREGATION AND HYBRID APPROACHES USING BPSO AND BGWO FOR FEATURE SELECTION FOR INDIAN STOCK INDEX PRICE PREDICTION USING GRU

Abstract

The stock exchange is a significant component of the economy, and forecasting its development is essential. Several deep-learning time series models based on RNN and its variants are used to forecast the stock market, but their accuracy still needs to be improved. Optimization strategies, such as GA, PSO, GWO, and others, have been used to enhance the reliability of these models. In this proposed paper, we have used the Binary GWO and Binary PSO algorithms to optimize the input characteristics of a two-layer GRU model for forecasting Indian stock index price. To increase the accuracy of these models, we have suggested two distinct approaches: Aggregation Method and the Hybrid method. In the 10- year time frame, the data indicated that the aggregation approach produced superior accuracy than standard Binary GWO and Binary PSO independently. Although the suggested hybrid method did not out perform individual Binary PSO methods in terms of accuracy, it performed better than the Binary GWO model in a 10- year time frame. Moreover, it showed promise for future improvement. The proposed approaches have the potential to make essential contributions to the index of stock price forecasting, particularly in the Indian market.