Time-Series Techniques for Forecasting the Unemployment Rate in China
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Abstract
This study aims to investigate forecasting methods for urban unemployment rates in China and to compare the accuracy of six time series forecasting techniques, namely Trend Analysis, Decomposition, Simple Moving Average, Single Exponential Smoothing, Double Exponential Smoothing, and Winter’s Method. The data used in this study consist of 60 monthly unemployment rate observations collected from January 2020 to December 2024. Minitab 16 was employed as the primary statistical tool to conduct the forecasting and evaluate the accuracy of each method using the Mean Absolute Percentage Error (MAPE) as the primary performance metric. The findings reveal that the most appropriate forecasting method for this dataset is the Winters Method, since it yields the lowest MAPE when compared with other methods applied in the study.
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