TY - CHAP

T1 - Employing a Novel Metaheuristic Algorithm to Optimize an LSTM Model

T2 - International Conference on Advances in Computing and Data Sciences

AU - Dastgerdi, Amin Karimi

AU - Mercorelli, Paolo

N1 - Conference code: 7

PY - 2023/1/1

Y1 - 2023/1/1

N2 - It has been long since researchers as well as investors and stakeholders who are actively pursuing financial markets are trying to analyze stock price movements and predict its trend more accurately. To minimize the forecasting risk and make the most profit, several methods have been used among which Deep Learning was at the center of attention in recent years. Deep learning techniques include analyses of historical data and recognizing patterns that can assist scientists to make a more precise prediction. This paper focuses on the application of a optimization approach called Neural Network Algorithm to optimize Long short-term Memory for the prediction of financial time series. The findings reveal that the utilization of an optimization technique such as Neural Network Algorithm to optimize Long Short-term Memory neural networks results in a notable improvement of 40%, 65%, 4%, and 85% in the MAPE, Theil U, R, and RMSE metrics, respectively. Consequently, this leads to even more accurate results and more precise predictions.

AB - It has been long since researchers as well as investors and stakeholders who are actively pursuing financial markets are trying to analyze stock price movements and predict its trend more accurately. To minimize the forecasting risk and make the most profit, several methods have been used among which Deep Learning was at the center of attention in recent years. Deep learning techniques include analyses of historical data and recognizing patterns that can assist scientists to make a more precise prediction. This paper focuses on the application of a optimization approach called Neural Network Algorithm to optimize Long short-term Memory for the prediction of financial time series. The findings reveal that the utilization of an optimization technique such as Neural Network Algorithm to optimize Long Short-term Memory neural networks results in a notable improvement of 40%, 65%, 4%, and 85% in the MAPE, Theil U, R, and RMSE metrics, respectively. Consequently, this leads to even more accurate results and more precise predictions.

KW - Engineering

KW - Deep Learning

KW - Time Series Forecasting

KW - LSTM

KW - Optimization

KW - Metaheuristic Algorithms

UR - http://www.scopus.com/inward/record.url?scp=85172195374&partnerID=8YFLogxK

U2 - 10.1007/978-3-031-37940-6_39

DO - 10.1007/978-3-031-37940-6_39

M3 - Article in conference proceedings

SN - 978-3-031-37939-0

T3 - Communications in Computer and Information Science

SP - 478

EP - 489

BT - Advances in Computing and Data Sciences

A2 - Singh, Mayank

A2 - Tyagi, Vipin

A2 - Gupta, P.K.

A2 - Flusser, Jan

A2 - Ören, Tuncer

PB - Springer International Publishing AG

CY - Cham

Y2 - 27 April 2023 through 28 April 2023

ER -