Forecasting analysis of consumer goods demand using neural networks and ARIMA

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)872-880
Number of pages9
JournalInternational Journal of Technology
Issue number5
Publication statusPublished - 2015


  • Consumer goods
  • Forecasting
  • Neural network


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