Assimilation of Principal Component Analysis and Wavelet with Kernel Support Vector Regression for Medium-Term Financial Time Series Forecasting
Baba Gimba Alhassan1, Fadhilah Binti Yusof2, Siti Mariam Norrulashikin3
1Baba Gimba Alhassan*, Department of Mathematical Sciences, Universiti Teknologi Malaysia (UTM), 81310 Johor Bahru, Malaysia Department of Statistics, School of Applied and Natural Sciences Federal Polytechnic Bida (FPB) Niger State Nigeria.
2Fadhilah Binti Yusof, Department of Mathematical Sciences, Universiti Teknologi Malaysia (UTM), 81310 Johor Bahru, Malaysia.
3Siti Mariam Norrulashikin, Department of Statistics, School of Applied and Natural Sciences Federal Polytechnic Bida (FPB) Niger State Nigeria.
Manuscript received on March 03, 2020. | Revised Manuscript Received on March 05, 2020. | Manuscript published on March 15, 2020. | PP: 40-48 | Volume-4 Issue-7, March 2020. | Retrieval Number: G0667034720/2020©BEIESP | DOI: 10.35940/ijmh.G0667.034720
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Entities and institutional financiers have gained a lot of growth from financial time series forecasting in recent times. But the major challenges of financial time series data are the high noise and complexity of its nature. Researchers in recent times have successfully engaged the application of support vector regression (SVR) to conquer this challenge. In this study principal component analysis (PCA) is applied to extract the low dimensionality and efficient feature information, while wavelet is used to pre-process the extracted features in other to nu1llify the influence of the noise in the features with a KSVR based forecasting model. The analysis is carried out based on the quarterly tax revenue data of 39 years from the first quarter of 1981 to the last quarter of 2016. The forecasting is made for ten quarters ahead. The initial empirical result shows that the multicollinearity has been reduced to zero (0), and the analytic result reveals that the proposed model PCA-W-KSVR outperforms KSVR, PCA-KSVR, and W-KSVR in terms of MAE, MAPE, MSE and RMSE
Keywords: Principal Component Analysis, Dimensionality, Financial time series, Forecasting, Tax revenue