A novel intuitionistic fuzzy time series prediction model with cascaded structure for financial time series
| Autor: | Ufuk Yolcu, OZGE CAGCAG YOLCU |
|---|---|
| Přispěvatelé: | CAĞCAĞ YOLCU Ö., YOLCU U. |
| Rok vydání: | 2023 |
| Předmět: |
COMPUTER SCIENCE
ARTIFICIAL INTELLIGENCE Intuitionistic fuzzy C-means Mühendislik ENGINEERING Financial time series algorithms Cascade forward neural network BİLGİSAYAR BİLİMİ YAPAY ZEKA Yapay Zeka Genel Mühendislik Artificial Intelligence Bilgisayar Bilimleri Engineering Computing & Technology (ENG) Bilgisayar Bilimi Uygulamaları Computer Sciences General Engineering Mühendislik Bilişim ve Teknoloji (ENG) COMPUTER SCIENCE Computer Science Applications Fizik Bilimleri Physical Sciences Engineering and Technology Bilgisayar Bilimi Mühendislik ve Teknoloji Prediction Algoritmalar |
| Zdroj: | Expert Systems with Applications. 215:119336 |
| ISSN: | 0957-4174 |
| DOI: | 10.1016/j.eswa.2022.119336 |
| Popis: | © 2022 Elsevier LtdFinancial time series prediction problems, for decision-makers, are always crucial as they have a wide range of applications in the public and private sectors. This study presents a cascaded intuitionistic fuzzy model for financial time series prediction. The proposed prediction model has the ability to jointly and simultaneously model linear and nonlinear relationships in financial time series. Thus, it can adapt itself to both linear and non-linear surfaces of the data and can produce satisfactory predictions for financial time series. Moreover, the other reason why to be produced better predictions, the proposed model reckons non-membership degrees in addition to membership degrees in the prediction process. With these aspects, the proposed prediction model is different and superior to all models in the literature. This superiority has been proven by the analysis of 48 different financial time series containing TAIEX, DIJ, SSEC, and IEX data sets. The results have been evaluated in terms of RMSE, MAPE, and MdRAE metrics and some other perspectives as well. The proposed prediction model has achieved progress in prediction performance, up to 80% for TAIEX 2000–2004 datasets, 60% for TAIEX 2008–2018 datasets, approximately 50% for DJI and SSEC, and up to 70% for IEX. All the discussed indicators demonstrated the outstanding prediction performance of the proposed cascaded intuitionistic prediction model compared to some other state-of-the-art prediction tools. |
| Databáze: | OpenAIRE |
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