Automatic identification of ARIMA models with neural network
نویسنده
چکیده
The paper investigates an artificial intelligence based demand forecasting method. A neural network driven automatic ARIMA model identification is being introduced. The limitations of the current methods are shown and a new identification concept is presented. It is being discussed that the model identification with a neural network is less sensitive to input errors through its intuitive capability, additionally after a certain number of training steps the algorithm is able to identify time series with unknown characteristics.
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