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- W415945829 abstract "The difficulty associated with building forecasting models for non-stationary and volatile data has necessitated the development and application of new sophisticated techniques that can handle such data. Interestingly, there are a lot of real-world phenomena where data that are “difficult to analyze” are generated. One of these is the stock market where data series generated are often hard to forecast because of their peculiar characteristics. In particular, the stock market has been referred to as a complex environment and financial time series forecasting is often tagged as the most challenging application of time series forecasting. In this study, a novel approach known as Support Vector Regression (SVR) for forecasting non-stationary time series was adopted and the feasibility of applying this method to five financial time series was examined. Prior to implementing the SVR algorithm, three different methods of transformation namely Relative Difference in Percentages (RDP), Z-score and Natural Logarithm transformations were applied to the data series and the best prediction results obtained along with the associated transformation technique was presented. Our study indicated that the Z-score transformation is the best scaling method for financial time series, exhibiting superior" @default.
- W415945829 created "2016-06-24" @default.
- W415945829 creator A5004917910 @default.
- W415945829 date "2006-01-01" @default.
- W415945829 modified "2023-09-27" @default.
- W415945829 title "Support Vector Regression for Non-Stationary Time Series" @default.
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