ANALYSIS OF FINANCIAL TIME SERIES WITH MODEL HYBRIDIZATION

Year 2017, , 331 - 341, 30.09.2017

Huseyin Ince Fatma Sonmez Cakir

https://doi.org/10.17261/Pressacademia.2017.700

Abstract

Purpose- The aim of this study is to obtain better estimation results by hybridizing
models that reveal linear and nonlinear relationships used in the intended
financial time series. 

Methodology- ARIMA and Artificial Neural Networks (ANN) models were used in estimating
NASDAQ stock market index values between 03.01.2012 and 30.06.2017 comparison
of hybrid model results with different ways of error determination in literature.

Findings- ARIMA residues have been tested different models where only
residues are used with basic indications, only residues and basic. The
calculation of residues was done separately with the addition and multiplication
function. These residues were modeled with ANN, and the obtained results are
collected and established hybrid model with ARIMA forecasts. When the results
obtained at the end of the operations are compared, it is seen that the product
function of some of the addition functions gives better results in some models.

Conclusion- The
hybridization of the ANN and NASDAQ index estimates with the ARIMA method
resulted in processing for both addition and multiplication functions. Residues
calculated with the addition model showed better results in ANN hybrid. What
variables are used to calculate residuals is that the hybrid model gives better
estimation results than single models.

Keywords

ARIMA, artificial neural network, hybrid models, time series analysis, forecasting

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