A fast and accurate model for forecasting wind speed and solar radiation time series based on extreme learning machines and principal components analysis

2014 ◽  
Vol 6 (1) ◽  
pp. 013114 ◽  
Author(s):  
Hassen Bouzgou
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Solar Radiation ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Extreme Learning Machines ◽  
Accurate Model ◽  
Learning Machines ◽  
Components Analysis ◽  
Radiation Time

scholarly journals Estimationof a Tax Compliance Index with Specific Application to Pay-As-You-Earn

2021 ◽  
Vol 04 (02) ◽  
Author(s):  
Cathrine T Koloane ◽  
Keyword(s):  
Time Series ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Composite Index ◽  
Tax Compliance ◽  
Upward Trend ◽  
Monthly Data ◽  
The World ◽  
The Future ◽  
Components Analysis

This article provides a composite index for Pay-As-You-Earn (PAYE)tax using Principal Components Analysis (PCA). The study uses time series from April 2012 to March 2020 (using monthly data) for the ratios derived from the four compliancemeasures namely, payments on time, registration on time, filing on time and accurate declarations. The index is computed using the weights of the four derived principal components. According to the model results, the PAYE tax compliance index averages around 75.0% for the period, with the lowest value of 72.3% in 2013/14 and the highestvalue of 77.1% achieved in 2018/19. There is a clear upward trend, indicating improving levels of compliance in PAYE. Similarly, setting the baseline index of 100 i.e. assuming 100% compliance for 2012/13, results in PAYE tax compliance index averaging around 101.6% for the period, with the lowest value of 97.72% in 2013/14 and the highest value of 104.26% achieved in 2018/19. The study recommends this methodology to be applied to all the tax products and that the overall tax compliance index be computed. This will assist tax authorities all over the world to actively monitor tax compliance levels and institute timeous corrective measures in order to address non-compliance and ultimately maximise PAYE revenue collections. Moreover, this study also serve as a base for many of the future tax compliance indices studies.

Analysing Spatial Time Series of Local Unemployment: A Graphical Approach Using Principal Components Analysis and Seasonal Adjustment Procedures

1987 ◽  
Vol 19 (2) ◽  
pp. 225-246
Author(s):  
R Dunn
Keyword(s):  
Time Series ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Seasonal Adjustment ◽  
Graphical Approach ◽  
Empirical Results ◽  
Adjustment Procedure ◽  
Graphical Displays ◽  
Components Analysis ◽  
Space Economy

In this paper, the potential of principal components analysis to analyse spatial time series of local unemployment is discussed. A seasonal adjustment procedure is also used which allows trend, seasonal, and irregular subcomponents of the unemployment series to be identified and analysed separately. It is argued that such an approach may provide important insights into the functioning of the regional space economy. Empirical results are presented from a study of local unemployment in the Severnside region. Throughout the paper emphasis is placed on graphical displays and summaries.

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