Hybrid evolutionary intelligent system and hybrid time series econometric model for stock price forecasting

Abstract

In this paper, a hybrid evolutionary intelligent system is proposed for dimensionality reduction and tuning the learnable parameters of artificial neural network (ANN) that can forecast the future (1‐day‐ahead) close price of the stock market using various technical indicators. Although the ANN possesses the ability to model highly uncertain and complex nonlinear data but the key challenge in ANN is tuning its parameters and minimizing the feature set that can be used in the input layer. The backpropagation approach used for training the ANN has a limitation to get trapped in local minima and overfitting the data. Motivated by this, we proposed a hybrid intelligent system for optimizing the initial parameters and for reducing the dimensions of the feature set. The proposed model is a combination of feature extraction technique, namely principal component analysis (PCA), particle swarm optimization (PSO), and Levenberg‐Marquardt (LM) algorithm for training the feed‐forward neural networks (FFNN). This paper also compares the forecasting efficiency of the proposed model with PSO‐FFNN, regular FFNN, two standard benchmark approaches viz. GA and DE and another hybrid model obtained by the combination of PCA and a time series econometric model viz. auto‐regressive distributed lag model. The presented technique has been tested to predict the close price of three stock indices viz. Nifty 50, Sensex, and S&P 500. Simulation results indicate that the proposed model shows superior forecasting accuracy as compared with other methods.

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

“…The authors of this paper used GWO to fine-tune the parameters of an ANN for forecasting stock prices over different periods. Kumar et al (2021c) presented a hybrid evolutionary ANN model by aggregating principal component analysis (PCA), PSO, and feed-forward neural network (FFNN) to foresee the close price of stock indices by employing 20 indicators. In this work, PCA is utilized for dimensionality reduction; PSO is applied to determine the optimum value of initial weights and bias of FFNN and obtained promising results.…”

An intelligent forecast for COVID‐19 based on single and multiple features

“…Gourav Kumar and Uday Pratap Singh established a hybrid time series econometric model to predict stock prices. 25 As the epidemic continues, the COVID‐19 has mutated, and the mutant strain is more contagious. However, many of the epidemic data used for visual analysis and modeling prediction are available at the beginning of the epidemic, and the results obtained are no longer applicable to the current epidemic situation.…”

“…At the same time, in other fields, there are also cases of using models to predict. Gourav Kumar and Uday Pratap Singh established a hybrid time series econometric model to predict stock prices 25 . As the epidemic continues, the COVID‐19 has mutated, and the mutant strain is more contagious.…”

Multihorizons transfer strategy for continuous online prediction of time‐series data in complex systems

“…Time-series prediction is an important direction of dynamic data analysis and processing, [1][2][3] and it has important application value in equipment health management, fault prediction, and system twinning in the engineering field. [4][5][6] In practice, the health status of the system is often evaluated based on higher-dimensional time-series data characteristics, [7][8][9] so as to realize the health management of the system.…”