Multivariate Time Series Data Forecasting Using Multi-Output NARNN Model

Abstract

This research proposes the multi-output Nonlinear Autoregressive Neural Network (NARNN) method to forecast multivariate time series data containing the input layer, one hidden layer, and the output layer. The multi-output NARNN method is performed by applying the logistic activation function and the resilient backpropagation learning algorithm. The stage of determining the input variable is chosen based on the number of data frequencies. The number of neurons in the hidden layer is half of the number of input variables. Simulation and empirical studies are conducted to test whether the proposed method works well for multivariate time series data forecasting. The simulation results show that the best performance is the simulation data generated from the MESTAR nonlinear model. The simulation study results are as expected. Empirical studies on Indonesia's inflation and Bank Indonesia interest rate data show that the multi-output NARNN method provides better forecasting accuracy than the VAR, VMA, and VARMA methods with a total MSE value of 0.054655 and a total MAPE of 0.026853 in the testing data.