### Forecasting Analysis of Consumer Goods Demand using Neural Networks and ARIMA

*Arian Dhini, Isti Surjandari, Muhammad Riefqi, Maya Arlini Puspasari*

**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.

**Keywords**: ARIMA; Consumer goods; Forecasting; Neural network

Full PDF Download

#### References

Adebanjo, D., Mann, R., 2000. Identifying Problems in Forecasting Consumer Demand in the Fast Moving Consumer Goods Sector. Benchmarking: An International Journal, Volume 7(3), pp. 223–230

Babu, C.N., Reddy, B.E., 2014. A Moving-average Filter Based Hybrid ARIMA–ANN Model for Forecasting Time Series Data. Applied Soft Computing, Volume 23, pp. 27–38

Bennell, J.A., Crabbe, D., Thomas, S., Ap Gwilym, O., 2006. Modelling Sovereign Credit Ratings: Neural Networks Versus Ordered Probit. Expert Systems with Applications, Volume 30(3), pp. 415–425

Cybenko, G., 1989. Approximation by Superpositions of a Sigmoidal Function. Mathematics of Control, Signals and Systems, Volume 2(4), pp. 303–314

Faruk, D.Ö., 2010. A Hybrid Neural Network and ARIMA Model for Water Quality Time Series Prediction. Engineering Applications of Artificial Intelligence, Volume 23(4), pp. 586–594

Hill, T., O'Connor, M., Remus, W., 1996. Neural Network Models for Time Series Forecasts. Management Science, Volume 42(7), pp. 1082–1092

Ho, S., Xie, M., 1998. The Use of ARIMA Models for Reliability Forecasting and Analysis. Computers and Industrial Engineering, Volume 35(1–2), pp. 213–216

Jian-Chang, L., Dong-xiao, N., Zheng-Yuan, J., 2004. A Study of Short-term Load Forecasting based on ARIMA-ANN. Proceedings of International Conference on Machine Learning and Cybernetics, Volume 5, pp. 3183–3187

Kaastra, I., Boyd, M., 1996. Designing a Neural Network for Forecasting Financial and Economic Time Series. Neurocomputing, Volume 10(3), pp. 215–236

Khashei, M., Bijari, M., 2010. An Artificial Neural Network (p, d, q) Model for Timeseries Forecasting. Expert Systems with applications, Volume 37(1), pp. 479–489

Kotsialos, A., Papageorgiou, M., Poulimenos, A., 2005. Holt-winters and Neural-Network Methods for Medium-term Sales Forecasting. The 16th IFAC World Congress, Prague, Czech Republic

Liker, J., 2004. The Toyota Way: 14 Management Principles from the World’s Greatest Manufacturer, McGraw-Hill

Ong, C.-S., Huang, J.-J., Tzeng, G.-H., 2005. Model Identification of ARIMA Family using Genetic Algorithms. Applied Mathematics and Computation, Volume 164(3), pp. 885–912

Rexhausen, D., Pibernik, R., Kaiser, G., 2012. Customer-facing Supply Chain Practices—The Impact of Demand and Distribution Management on Supply Chain Success. Journal of Operations Management, Volume 30(4), pp. 269–281

Taskaya-Temizel, T., Ahmad, K., 2005. Are ARIMA Neural Network Hybrids Better than Single Models?, In: Proceedings of the IEEE International Joint Conference on Neural Networks, Volume 5, pp. 3192–3197

Zhang, G.P., 2001. An Investigation of Neural Networks for Linear Time-series Forecasting. Computers and Operations Research, Volume 28(12), pp. 1183–1202

Zhang, G.P., 2003. Time Series Forecasting using a Hybrid ARIMA and Neural Network Model. Neurocomputing, Volume 50, pp. 159–175