# What ARIMA data to feed to neural networks in hybrid model for forecasting?

In the hybrid forecasting model using ARIMA and neural networks (multilayer perceptron), the time series is first processed in ARIMA for linear processing. You get forecast values and also statistical values as residuals, various errors measures.

My question is: what items do you use to feed to neural networks as you need at least two items for the input layer: input and "desired output"?

Once you have done that, you are back to a standard neural network. For time series, one way to train the network is, for each time t, to use $x_{t-1}$ to $x_{t-n}$ (with n defining some reasonable window) as inputs and $x_t$ as output. Your neural network can then be used to predict the value one period ahead using n observations in the past. Several other questions/answers on this site provide relevant material: