Smoothing in Neural Network for Univariat Time Series Data Forecasting

Abstract

Time series data is interesting research material for many people. Not a few models have been produced, but very optimal accuracy has not been obtained. Neural network is one that is widely used because of its ability to understand non-linear relationships between data. This study will combine a neural network with exponential smoothing to produce higher accuracy. Exponential smoothing is one of the best linear methods is used for data set transformation and thereafter the new data set will be used in training and testing the Neural Network model. The resulting model will be evaluated using the standard error measure Root Mean Square Error (RMSE). Each model was compared with its RMSE value and then performed a T-Test. The proposed ES-NN model proved to have better predictive results than using only one method.