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Analysis of financial data is always challenging due to the non-linear and non-stationary characteristics of the time series which is further complicated by volatility clustering effect and sudden changes such as jump, steep slopes and valleys. Classical regression based analysis techniques often entail rigorous mathematical treatments albeit with little success in exploiting the differing frequency characteristics to uncover hidden but valuable trending information. Wavelet, on the other hand provides an efficient way to represent time series with such complex dynamics by decomposing it into time-frequency space and at the same time preserve both temporal and spectral information. This property enables analysts to identify the dominant modes (spectral information) of a time series and observe how those modes vary over time (temporal information). Most importantly, wavelet transform is computationally efficient, only a small number of wavelet coefficients are needed to describe complicated signals. This paper seeks to establish cases for the use of wavelets as viable tools in time series forecasting. Two time series, Kijang Emas Daily Index and Bit Coin Daily Price with differing characteristics are used as subjects of study. Out-of-sample dynamic forecasting of 20 points is made using best-fit ARIMA and prior-point imitation follow by wavelet de-noising methods (imitate-wavelet). Comparisons made with MAPE measurements of ARIMA and imitate-wavelet methods indicated comparable forecasting performance between the simpler imitate-wavelet techniques and ARIMA model.

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High dimensional data always lead to overfitting in the prediction model. There are many feature selection methods used to reduce dimensionality. However, previous studies in this area of research have reported that an imbalanced class raises another issue in the prediction model. The existence of the imbalanced class can lead to low accuracy in the minority class. Therefore, high dimensional data with imbalanced class not only increase the computational cost but also reduce the accuracy of the prediction model. Handling imbalanced class in high dimensional data is still not widely reported in the literature. The objective of the study is to increase the performance of the prediction model. We increased the sample size using the Synthetic Minority Oversampling Technique (SMOTE) and performing the dimension reduction using minimum redundancy and maximum relevance criteria. The support vector machine (SVM) classifier was used to build the prediction model. The leukaemia dataset was used in this study due to its high dimensionality and imbalanced class. Consistent with the literature, the result shows that the performance of the shortlisted features is better than those without undergoing the SMOTE. In conclusion, a better classification result can be achieved when high dimensional feature selection coupled with the oversampling method. However, there are certain drawbacks associated with the use of a constant amount of synthesis of SMOTE, further study on different amounts of synthesis might provide different performances.

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