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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 c...

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Detalles Bibliográficos
Autores principales: Kumar, Gourav, Singh, Uday Pratap, Jain, Sanjeev
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9415266/
https://www.ncbi.nlm.nih.gov/pubmed/36043118
http://dx.doi.org/10.1007/s00500-022-07451-8
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author Kumar, Gourav
Singh, Uday Pratap
Jain, Sanjeev
author_facet Kumar, Gourav
Singh, Uday Pratap
Jain, Sanjeev
author_sort Kumar, Gourav
collection PubMed
description 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.
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spelling pubmed-94152662022-08-26 An adaptive particle swarm optimization-based hybrid long short-term memory model for stock price time series forecasting Kumar, Gourav Singh, Uday Pratap Jain, Sanjeev Soft comput Soft Computing in Decision Making and in Modeling in Economics 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. Springer Berlin Heidelberg 2022-08-26 2022 /pmc/articles/PMC9415266/ /pubmed/36043118 http://dx.doi.org/10.1007/s00500-022-07451-8 Text en © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022, Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Soft Computing in Decision Making and in Modeling in Economics
Kumar, Gourav
Singh, Uday Pratap
Jain, Sanjeev
An adaptive particle swarm optimization-based hybrid long short-term memory model for stock price time series forecasting
title An adaptive particle swarm optimization-based hybrid long short-term memory model for stock price time series forecasting
title_full An adaptive particle swarm optimization-based hybrid long short-term memory model for stock price time series forecasting
title_fullStr An adaptive particle swarm optimization-based hybrid long short-term memory model for stock price time series forecasting
title_full_unstemmed An adaptive particle swarm optimization-based hybrid long short-term memory model for stock price time series forecasting
title_short An adaptive particle swarm optimization-based hybrid long short-term memory model for stock price time series forecasting
title_sort adaptive particle swarm optimization-based hybrid long short-term memory model for stock price time series forecasting
topic Soft Computing in Decision Making and in Modeling in Economics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9415266/
https://www.ncbi.nlm.nih.gov/pubmed/36043118
http://dx.doi.org/10.1007/s00500-022-07451-8
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