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Forecasting future values of economic variables is one of the most critical tasks for governments, especially the values related to customs revenue collection are to be forecasted efficiently as the need for planning is great in this sector, because it is considered one of the sources of funding for the state's public treasury. The main objective of this research is to identify an appropriate statistical model for time series forecasting customs revenue collection during the current COVID-19 pandemic in Libya. The decision throughout this research is mainly concerned with ARIMA model, and Simple, Brown’s linear trend, exponential smoothing methods. The obtained data covers 108 observations, starting from the first week of the 6th month of the year 2019 to the last week of the 8th month of the year 2021.Based on the forecasting results of the current research, it was revealed that ARIMA (0,1,1) model offered more probabilistic information that improves forecasting the volume of customs revenue collection in light of the COVID-19 pandemic. According to this model, the research forecasts the new period in the next eight weeks or two months and finds that it will be increasing. In this research, ARIMA model and exponential smoothing methods are linear models based on the reactions to customs revenue collection due to the spread of the COVID-19 pandemic in the world. Furthermore, the forecasting performance between linear and nonlinear models can be compared in future studies.

The current paper reports an empirical study on the use of a composite model for developing a statistical model to predict the value of imports of the crude material in Malaysia. It is a combination of both regression and autocorrelation integrated moving average (ARIMA) models to produce a better forecast. The study reported in this paper mainly aimed at comparing the two composite models: the first model (Without Regression Processing) and the second model (With Regression Processing). The initialization set of the data has 91 data points, starting from first quarter 1991 to third quarter 2013. The general steps followed in comparing the two models were test significance of parameters and the minimum value of the statistical measure of forecast error (Thiel Coefficient) of its forecasting power. The results showed the composite model (With Regression Processing) had better significant and a lower percentage of U-statistics. This implies that the model had a substantially better fit.