Adaptive Stock Forecasting Model using Modified Backpropagation Neural Network (MBNN)

Uma Gurav, Nandini Sidnal

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

Time series based model has been widely applied to estimate the future stock price, and aids investors' decisions and trades. However, due to high rate of volatility and non-linearity of time series, affecting stock market forecasting. To address this, artificial neural network (ANN) and deep neural network (DNN) have been applied in the area of stock price forecasting by various researchers. However, existing model use ANN and DNN with back propagation fails to provide flexible linear or nonlinear relationship among variables and they are difficult to train. The objective of this work is to present a modified back propagation neural network (MBNN) model that can handle huge density of nonlinear data, their relationship and give an optimal strategy for computationally hard problem. Experiments are conducted to evaluate performance of proposed MBNN over existing model in terms RMSE and MAPE. The outcome shows significant performance improvement by MBNN over state-of-art approach.

Original languageEnglish
Title of host publicationProceedings of the International Conference on Computational Techniques, Electronics and Mechanical Systems, CTEMS 2018
EditorsS. K. Niranjan, Veena Desai, Vijay S. Rajpurohit, M N Nadkatti
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages380-385
Number of pages6
ISBN (Electronic)9781538677094
DOIs
Publication statusPublished - Dec 2018
Externally publishedYes
Event1st International Conference on Computational Techniques, Electronics and Mechanical Systems, CTEMS 2018 - Belagavi, India
Duration: 21 Dec 201823 Dec 2018

Publication series

NameProceedings of the International Conference on Computational Techniques, Electronics and Mechanical Systems, CTEMS 2018

Conference

Conference1st International Conference on Computational Techniques, Electronics and Mechanical Systems, CTEMS 2018
Country/TerritoryIndia
CityBelagavi
Period21/12/1823/12/18

Keywords

  • Artificial neural network
  • Data mining
  • Deep learning
  • machine learning
  • prediction system
  • time series

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