An adaptive particle swarm optimization-based hybrid long short-term memory model for stock price time series forecasting.

In this paper, we presented a long short-term memory (LSTM) network and adaptive particle swarm optimization (PSO)-based hybrid deep learning model for forecasting the stock price of three major stock indices such as Sensex, S&P 500, and Nifty 50 for short term and long term. Although the LSTM can handle uncertain, sequential, and nonlinear data, the biggest challenge in it is optimizing its weights and bias. The back-propagation through time algorithm has a drawback to overfit the data and being stuck in local minima. Thus, we proposed PSO-based hybrid deep learning model for evolving the initial weights of LSTM and fully connected layer (FCL). Furthermore, we introduced an adaptive approach for improving the inertia coefficient of PSO using the velocity of particles. The proposed method is an aggregation of adaptive PSO and Adam optimizer for training the LSTM. The adaptive PSO attempts to evolve the initial weights in different layers of the LSTM network and FCL. This research also compares the forecasting efficacy of the proposed method to the genetic algorithm (GA)-based hybrid LSTM model, the Elman neural network (ENN), and standard LSTM. Experimental findings demonstrate that the suggested model is successful in achieving the optimum initial weights and bias of the LSTM and FC layers, as well as superior forecasting accuracy.