Rainfall and potential evapotranspiration are important variables in water balance study. Rainfall data were obtained from Malaysian Meteorological Department while estimates of potential evapotranspiration were calculated using Penman-Monteith method. Trend analysis of monthly and annual rainfall, potential evapotranspiration and rainfall deficit are essential to manage irrigation system in agricultural systems. This is because changes in trend of these parameters may affect the water cycle and ecosystem. Annual and monthly values of these variables were analysed from 1980-2009. Results indicated  increasing trends of 16.2 mm yr-1 and 3.01 mm yr-1 for both annual rainfall and potential evapotranspiration, respectively. Consequently, these trends resulted in annual rainfall deficit of 1.69 mm per year.

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Principal component analysis (PCA) guided clustering approach is widely used in high dimensional data to improve the efficiency of K-means cluster solutions. Typically, Pearson correlation is used in PCA to provide an eigenanalysis to obtain the associated components that account for most of the variations in the data. However, PCA based Pearson correlation can be sensitive on non-Gaussian distributed data, which involve skewed observations such as outlying values. Thus, applying PCA based Pearson correlation on such data could affect cluster partitions and generate extremely imbalanced clusters in a high dimensional space. In this study, Tukey's biweight correlation based on Mestimate approach in PCA is used as an alternative to Pearson correlation. This approach is more resistant to outlying values as it examines each observation and down weight those that lie far from the center of the data. In particular two major features are highlighted: (1) fewer components are retained and imbalanced clusters at the recommended cumulative percentage of variation threshold is avoided; (2) the cluster quality with respect to external, internal and relative criteria as shown in Rand, Silhouette and Davies-Bouldin indices, outperform that of the clusters from PCA based Pearson correlation. General TermsData Structures and Algorithms.

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

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