Open Access Open Access  Restricted Access Subscription or Fee Access

Hierarchical Forecasting Method Based on ARIMAX and Recurrent Neural Network for Motorcycle Sales Prediction

I. G. S. A. Prayoga, Suhartono, S.P. Rahayu

Abstract



Motorcycle sales forecast is needed by motorcycle distributors to predict the market share and determine sales target. The purpose of this study is to forecast motorcycle sales in Pacitan, Indonesia.There are four variables to be forecasted, i.e., Matic, Cub, Sport and total sales.Motorcycle sales data has a hierarchical structure, where total sales is the aggregation of Matic, Cub and Sport sales. Consequently, it needs to be forecasted by using hierarchical method. The first step of hierarchical forecasting is obtaining individual forecast of each variable.In this step, we have compared ARIMAX and Recurrent Neural Network (RNN), and we get that RNN is better than ARIMAX, so the individual forecast is calculated by using RNN.The individual forecast does not have a hierarchical structure, so it must be revised.To revise it, we use three different methods, i.e., bottom-up, top-down and optimal combination. The results show that the best forecast for total and Matic sales are produced by top-down method, while the best forecast for Cub and Sport sales, respectively are produced by bottom-up and optimal combination method. Moreover, motorcycle sales are predicted to increase continuously and reach high sales in the months around the Eidal-Fitr holidays, except the Cub sales which are predicted to be stationary with seasonal pattern.

Keywords


motorcycle sales, ARIMAX, recurrent neural network, bottom-up, top-down, optimal combination.

Full Text:

PDF


Disclaimer/Regarding indexing issue:

We have provided the online access of all issues and papers to the indexing agencies (as given on journal web site). It’s depend on indexing agencies when, how and what manner they can index or not. Hence, we like to inform that on the basis of earlier indexing, we can’t predict the today or future indexing policy of third party (i.e. indexing agencies) as they have right to discontinue any journal at any time without prior information to the journal. So, please neither sends any question nor expects any answer from us on the behalf of third party i.e. indexing agencies.Hence, we will not issue any certificate or letter for indexing issue. Our role is just to provide the online access to them. So we do properly this and one can visit indexing agencies website to get the authentic information.