Skip to main content
main-content
Top

Hint

Swipe to navigate through the articles of this issue

23-11-2020 | Issue 6/2021

The Journal of Supercomputing 6/2021

A new deep intuitionistic fuzzy time series forecasting method based on long short-term memory

Journal:
The Journal of Supercomputing > Issue 6/2021
Authors:
Cem Kocak, Erol Egrioglu, Eren Bas
Important notes

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Abstract

In recent years, deep artificial neural networks can have better forecasting performance than many other artificial neural networks. The long short-term memory (LSTM) is one of the deep artificial neural networks. There have been a few fuzzy time series forecasting model based on LSTM in the literature. However, LSTM has not been used in an intuitionistic fuzzy time series (IFTS) forecasting method until now. In this paper, determining the fuzzy relations is made by using the LSTM artificial neural network and so, a new intuitionistic fuzzy time series forecasting method based on LSTM is proposed. In the proposed method, obtaining the membership and non-membership values is performed by using intuitionistic fuzzy c-means. Then, the inputs of the LSTM are merged membership and non-membership values by a minimum operator. In this way, lagged crisp values are inputs of the long short-term memory. So, the proposed method is a high-order IFTS model. The architecture of the LSTM artificial neural network includes multiple inputs and a single output. The proposed method and some other methods in the literature are applied to the Giresun Temperature data and the Nikkei 225 stock exchange time series, and the forecasting performance of these methods is compared.

Please log in to get access to this content

To get access to this content you need the following product:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 58.000 Bücher
  • über 300 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Testen Sie jetzt 30 Tage kostenlos.

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 50.000 Bücher
  • über 380 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Umwelt
  • Maschinenbau + Werkstoffe




Testen Sie jetzt 30 Tage kostenlos.

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 69.000 Bücher
  • über 500 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Umwelt
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Testen Sie jetzt 30 Tage kostenlos.

Literature
About this article

Other articles of this Issue 6/2021

The Journal of Supercomputing 6/2021 Go to the issue

Premium Partner

    Image Credits