Roll motion prediction using a hybrid deep learning and ARIMA model

Abstract

Autoregressive Integrated Moving Average (ARIMA) is one of the linear model that is good, flexible, and easy to use in univariate time series analysis and forecasting. Some research activities in time series forecasting also suggest Artificial Neural Network (ANN) model as an alternative nonlinear model for forecasting. ARIMA model has a good ability to capture the linear pattern while the ANN model is good to capture the nonlinear pattern. ARIMA and ANN models have been widely used in the prediction of roll motion. ARIMA and ANN can also be combine as a hybrid model to take advantage of the ability of ARIMA and ANN models in linear and nonlinear modeling compared to ARIMA and ANN model. In this paper, we adapt the hybrid methodology to combine ARIMA and Deep Neural Network (DNN) model, an ANN model with multiple hidden layers. The real dataset used is the roll motion of a Floating Production Unit (FPU). The empirical results show that the DNN-ARIMA hybrid model is the best model for predicting the roll motion compared to the non hybrid models and very effective to improve forecast accuracy.