Hybrid Forecasting of Financial Time Series Using Adaptive Regression Splines and Exponential Smoothing
Abstract
The purpose of forecasting models premised on time series (TS) is to generate predictive models that provide a closer approximation of future data, with an acceptable error. A proposed framework is developed within the framework of this study as a hybrid model to improve the forecasting accuracy of the traditional TS models based on the Multivariate Adaptive Regression Splines (MARSplines) technique. The integration targets to take advantage of the flexibility and adaptation of MARSplines in the TS. In order to compare the performance of the suggested model, historical data over a six-year period of the Borsa Istanbul were used as a case study. The results indicate that the combined regression-based method gives a significant increase in the predictive capabilities and practical importance of the time series model.
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