Pythagorean fuzzy time series approach based on hybrid high-order artificial neural network for nonlinear time series forecasting
Ozge Gundogdu, Erol Egrioglu, Eren Bas · 2026
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Description
Ozge Gundogdu, Erol Egrioglu, and Eren Bas propose in 2026 a forecasting method for nonlinear time series that blends Pythagorean fuzzy sets with a hybrid neural network. Fuzzy methods capture uncertainty well, yet their inputs grow large and lag choice is often unsystematic. Dimension reduction first compresses membership data into cleaner inputs.
A modified network paired with simple smoothing then models both linear and nonlinear patterns, with swarm optimisation setting all parameters. Tests on rolling windows of two major stock indices beat existing approaches. Readers in finance and engineering gain a systematic, ready-to-build forecasting framework.
Catalog record
| CREATOR | Ozge Gundogdu, Erol Egrioglu, Eren Bas |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 49788 |
| LICENSE | https://creativecommons.org/licenses/by/4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |