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

Tax revenue modelling and forecasting is very crucial for revenue collection and tax administration management. The dynamics of heteroscedasticity in the financial time series (tax revenue) in the domain of technique used to model and predict tax revenue in the emerging economy threw us to this investigation. The reviews are categorized into two the tax revenue and stock exchange index. Five factors were considered in this studies modelling, forecasting, linear model, nonlinear model and heteroscedasticity, it is on this note that we syntheses over 75 studies from the literature to consider the pattern of reporting tax revenue and stock market index. Thus, from the reviewed literature, we inferred that the pattern of reporting tax revenue data and the analytical techniques employed by most of these studies are responsible for the instability (volatility) in the financial time series forecasting. Also, results revealed that linear models are mostly applied to tax revenue data with fewer non-linear models, while combination and single non-linear models were mostly used for stock exchange data. Thus, we recommend the combination of linear and nonlinear models for both tax revenue and stock exchange data which can minimize the error of heteroscedasticity in the forecasting of tax revenue in a developing economy.

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