Abstract—In the business world, the need for the availability of goods is critical, especially in motorcycle workshops. The goods availability is related to problems with customer trust, loss of capital, and storage warehouse capacity. Therefore, the ability of decision makers to predict the number of sales in the coming period is essential to be able to determine the procurement of goods more precisely. There is a method called Auto-Regressive Integrated Moving Average (ARIMA). This method is one model that can be used to forecast sales based on sales time series data in previous periods. The forecasting implementation with the ARIMA model can be done using the Pmdarima 1.1.0 library for Python. The test in this study uses sales data of 62 motorcycle parts from January 2017 to February 2019. Forecasting is done to help decision-makers in determining the amount of procurement of goods to meet the sales of the next three periods.
Index Terms—Auto-regressive integrated moving average, forecasting, motorcycle parts, time series.
Martinus Maslim, Ernawati, and Komang Arinanda are with the Department of Informatics Engineering, Universitas Atma Jaya Yogyakarta, Babarsari Street No. 43, Yogyakarta, Indonesia (e-mail: martinus.maslim@uajy.ac.id, ernawati@uajy.ac.id, 150708161@students.uajy.ac.id).
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Cite:Martinus Maslim, Ernawati, and Komang Arinanda, "Motorcycle Parts Sales Forecasting Using Auto-Regressive Integrated Moving Average Model," International Journal of Computer Theory and Engineering vol. 12, no. 1, pp. 28-31, 2020.
Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
