Vol 6, No 5 (2015) > Industrial Engineering >

Forecasting Analysis of Consumer Goods Demand using Neural Networks and ARIMA

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



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

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