Adaptive optimization-based Deep Convolutional Long Short-Term Memory for Bank NIFTY index prediction

Abstract

Bank NIFTY index prediction is a challenging problem, which dictates that the market is highly stochastic, and there are temporally dependent predictions from chaotic data. Thus, the development of an effective prediction model is required as the basic necessity and in this paper, the Bank NIFTY index prediction system is developed using the Deep Convolutional Long Short-Term Memory (Deep-ConvLSTM) model that effectively predicts the Bank NIFTY index. The overall procedure of the proposed approach involves three steps. The initial step is feature extraction, the second step is clustering, and the tertiary step is the prediction. The input data is fed to the feature extraction step. Here, the feature extraction is performed based on the technical indicators, and then the clustering is done based on modified Sparse Fuzzy [Formula: see text]-Means (FCM) in order to find the effective features. Finally, the prediction is carried out based on Deep-ConvLSTM model, which is trained optimally using the proposed Adaptive-Rider-Monarch Butterfly Optimization (Adaptive-Rider-MBO) for performing accurate prediction. The performance of the Bank NIFTY index prediction based on Adaptive-Rider-MBO is evaluated based on Mean Square Error (MSE) and Root Mean Square Error (RMSE). The proposed method achieves the minimal MSE of 2.010 and minimal RMSE of 1.418 based on the NIFTY Midcap 100 index.