THE EFFECT OF KERNEL VALUES IN SUPPORT VECTOR MACHINE TO FORECASTING PERFORMANCE OF FINANCIAL TIME SERIES

Yıl 2019, Cilt: 4 Sayı: 1, 17 - 21, 30.06.2019

Aytaç Altan , Seçkin Karasu

Öz

Nowadays, one of the most
important research topics in economic sciences is the estimation of different
financial exchange rates. The reliable and accurate forecasting of the exchange
rate in the financial markets is of great importance, particularly after the
recent global economic crises. In addition, the high accuracy forecasting of
the financial exchange rates causes that investors are less affected by
financial bubbles and crashes. In this paper, a financial time series
forecasting model is identified by support vector machine (SVM), which is one
of the machine learning methods, for estimating the closing price of USD/TRY
and EUR/TRY exchange rates. The closing price values and commodity channel index
(CCI) indicator value are used as inputs in financial time series forecasting
model. Various models are obtained with different kernel scale values in SVM
and the model that estimates financial time series with the highest accuracy is
proposed. The performance of the obtained models is measured by means of
Pearson correlation and statistical indicators such as mean absolute error
(MAE), mean squared error (MSE), and root mean squared error (RMSE). It is seen
that the forecasting performance of the proposed SVM model for the financial
time series data set is higher than that the performance of the compared other
models.  

Anahtar Kelimeler

Financial Time Series, Support Vector Machine, Kernel Scale, Exchange Rate Forecasting

Kaynakça

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