A dynamic hybrid model based on wavelets and fuzzy regression for time series estimation

TL;DR

This paper introduces a dynamic hybrid model combining wavelet decomposition and fuzzy regression to improve financial time series estimation, demonstrating its effectiveness on the S&P 500 index.

Contribution

It presents a novel hybrid approach integrating wavelets and fuzzy logic for financial time series estimation, enhancing prediction accuracy.

Findings

Hybrid model outperforms individual methods

Effective on S&P 500 index data

Shows improved estimation accuracy

Abstract

In the present paper, a fuzzy logic based method is combined with wavelet decomposition to develop a step-by-step dynamic hybrid model for the estimation of financial time series. Empirical tests on fuzzy regression, wavelet decomposition as well as the new hybrid model are conducted on the well known $SP500$ index financial time series. The empirical tests show an efficiency of the hybrid model.