马来西亚交通部门劳动力建模(考虑到Covid-19大流行)

Mohd Fikri Hadrawi, S. Shariff, Nur Ashikin Muhamad, Nurin Alya Abdullah, Nurshafiqah Ahmad Damanhuri
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引用次数: 0

摘要

新冠肺炎大流行令劳动力感到担忧,特别是在交通部门,因为交通一直是马来西亚的关键部门之一。在2019冠状病毒病大流行期间,失业问题在很大程度上导致马来西亚人经济状况不佳,尤其是在迫切需要变革的情况下。因此,需要采取战略方法来规划和管理劳动力趋势,以防止经济下滑。本研究考察了马来西亚交通部门的劳动力模式,使用时间序列模型进行比较,并使用最合适的时间序列模型进行预测。它明确地研究了马来西亚从2010年到2020年的进出口数量和马来西亚从2012年到2020年的运输部门的劳动力数量。这些数据被用来模拟和预测马来西亚运输部门的进出口数量和工人人数。研究发现,ARIMA(0,1,1)模型能够根据RMSE的值得出2020年马来西亚出口量的预测值,而Holt (α = 0.34, β = 0.01, γ = 0.3)模型能够在考虑MAE和MAPE值的情况下预测马来西亚出口量。此外,当使用MAE和RMSE时,ARIMA(2,1,3)模型能够产生2020年马来西亚进口量的预测值,而当考虑MAPE值时,Holt的模型(α = 0.41, β = 0.04, γ = 0.5)。最后,在考虑MAE值的同时,使用RMSE和MAPE模型(α =0.62, β = 0.00000000000000034694),采用ARIMA(1,1,1)作为预测马来西亚2020年交通运输部门工人数量的选择标准。
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Modelling Workforce For Transportation Sector In Malaysia (Considering Covid-19 Pandemic)
The Covid-19 pandemic is worrying the workforce, especially in the transportation sector since transportation has been one of Malaysia's crucial sectors. The problem of losing jobs during the Covid-19 pandemic largely contributes to low economic Malaysians, especially in the urgent need for change. Thus, adopting a strategic approach is needed to plan and manage workforce trends to prevent a drop in the economy. This study examines the workforce pattern in the transportation sector in Malaysia, comparing them using time series models and forecasting them using the best fit time series model. It studies explicitly the export and import volume in Malaysia from the year 2010 until 2020 and the number of workforces in the transportation sector in Malaysia from 2012 until 2020. The data were used to model and forecast the export and import volume and the number of workers in the transportation sector in Malaysia. It is found that ARIMA (0, 1, 1) model was able to produce the forecasted values for the year 2020 for export volume in Malaysia based on the values of RMSE and Holt’s (α = 0.34, β = 0.01, γ = 0.3) were able to forecast for export volume in Malaysia when the MAE and MAPE values were considered. Also, it is found that ARIMA (2, 1, 3) model was able to produce the forecast value for import volume in Malaysia for 2020 when the MAE and RMSE were used while Holt’s model (α = 0.41, β = 0.04, γ = 0.5) when MAPE value was considered. Lastly, ARIMA (1,1,1) was used as the selection criteria for forecasting the number of workers in the transportation sector in Malaysia for 2020 when RMSE and MAPE were used Holt’s (α =0.62, β = 0.00000000000000034694) model meanwhile when MAE value was considered.
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