Dynamic Neural Network for Business and Market Analysis

Javier de Arquer Rilo, Abir Hussain, May Al-Taei, Thar Baker, Dhiya Al-Jumeily

Research output: Chapter in Book/Conference proceeding with ISSN or ISBNConference contribution with ISSN or ISBNpeer-review


The problem of predicting nonlinear and nonstationary signals is complex since the physical law that controls them is unknown and it is complicated to be considered. In these cases, it is necessary to devise nonlinear models that imitate or learn the rules of behavior of the problem and can be developed based on historical data. For this reason, neural networks are useful tools to deal with this type of problem due to their nonlinearly and their capacity of generalizing. This paper aims at exploring various types of neural network architectures and study their performance with time series predictions. Predictions on two sets of data (of a very different nature) will be made using three neural networks including multilayer perceptrons, recurrent neural network and long-short term memory varying some important parameters: input neurons, epochs and the anticipation with which the predictions are made. Then, all results will be compared using standard metrics. As a conclusion, the influence of the type of series under study is more important than the parameters considered in what concerns the performance. The management of the memory in the networks is a key to its success in the prediction of S&P 500 and electrical power time series.

Original languageEnglish
Title of host publicationIntelligent Computing Theories and Application - 15th International Conference, ICIC 2019, Proceedings
EditorsDe-Shuang Huang, Vitoantonio Bevilacqua, Prashan Premaratne
Number of pages11
ISBN (Print)9783030267629
Publication statusPublished - 24 Jul 2019
Event15th International Conference on Intelligent Computing, ICIC 2019 - Nanchang, China
Duration: 3 Aug 20196 Aug 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11643 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference15th International Conference on Intelligent Computing, ICIC 2019

Bibliographical note

Publisher Copyright:
© 2019, Springer Nature Switzerland AG.


  • ANN
  • Long short-term memory
  • Neural Turing Machine


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