Forecasting Analysis of Consumer Goods Demand using Neural Networks and ARIMA

Abstract

Accurate forecasting of consumer demand for goods is extremely important as it allows companies to provide the right amount of goods at the right time. Autoregressive integrated moving average (ARIMA) is a popular method for forecasting time series data, and previous studies have shown that ARIMA can produce fairly accurate forecasting results. On the other hand, the neural network method has advantages in detecting non-linear patterns in data. In addition to these methods, the hybrid method, which combines the ARIMA and neural network methods, was applied in this study. A comparison analysis was conducted to determine the best performing model. In this study, the neural network model was found to be the most accurate.

Utilizing an Intervention Forecasting Approach to Improve Reefer Container Demand Forecasting Accuracy: A Case Study in Indonesia

“…In order to gain more power to apply these findings to other cases, repetitive research or longitudinal research is required. In addition, future research could be extended for comparison against other product categories, comparisons of different product life cycles, or comparisons with other forecasting methods, such as ANN or the regression method (Jaipuria and Mahapatra, 2014;Dhini et al, 2015).…”

“…While qualitative forecasting is only used when the amount of historical data is limited, quantitative forecasting is more commonly used among practitioners. The types of most commonly used quantitative forecasting are time series, regression model (Taylor and Letham, 2018), Autoregressive Integrated Moving Average (ARIMA) (Min, 2008;Jaipuria and Mahapatra, 2014;Dhini, 2015), Seasonal Autoregressive Integrated Moving Average (SARIMA) (Farhan and Ong, 2018;Mo et al, 2018), Artificial Neural Network (ANN) (Jaipuria and Mahapatra, 2014;Dhini et al, 2015); and Multinomial Logit Model (MNL) (Lubis et al, 2019). In circumstances of promotion or irrational events, combining qualitative and quantitative methods could be implemented to increase forecast accuracy (Min, 2008;Jaipuria and Mahapatra, 2014;Khamphinit and Ongkunaruk, 2016;Chong et al, 2017).…”

Reviewing Government Policy on Rice Price: Study Through Rice Price In East Java Market

“…Its significant contribution to inflation and its rapid response to various shocks make it feasible to be used as inflation leading indicators [11]. [12] research results also show that the prices of food commodities tend to increase by 5-12 percent per year during the period 1999-2011. Some food commodities that are important in controlling inflation and tend to experience price increases are rice and from spices.…”

Peramalan Jumlah Mahasiswa Baru Dengan Model Autoregressive Integrated Moving Average (Arima)

“…Model ARIMA ini juga banyak dipakai untuk pemodelan di berbagai macam bidang seperti : Abdullah L memprediksikan harga emas koin bullion [1]; Mohamad As'ad meramalkan puncak permintaan listrik harian di Australia [2]; Dhini A. at al. meramalkan permintaan makanan oleh konsumen [3]; Guha dan Bandyopadhyay meramalkan harga emas [4]; Mondal et al meramalkan harga saham [5]; Sarpong S. A. Meramalkan tingkat angka kematian [6]; Wabomba et al meramlkan produk domestik bruto [7]. Dengan demikian model ini layak digunakan untuk meramalkan jumlah mahasiswa baru tersebut.…”