Prediction of Engine Demand with a Data-driven Approach

Models predicting volume of engine demand from historical data are developed. To accommodate seasonal effects, neural networks and autoregressive integrated moving average (ARIMA) approaches are considered. Previous research on the effectiveness of neural networks to model phenomena with seasonality and trend using raw data has been inconclusive. In this paper, four predictive models for a linear time series with seasonality are developed and their accuracy is studied. Performance of a dummy variable linear regression model, a seasonal ARIMA model, a neural network model using raw historical data, and a hybrid linear model is compared. The seasonal ARIMA and linear regression models are found to perform better than the neural network model. The hybrid linear model is found to outperform the three individual models.