Abstract
Artificial Neural Network (ANN) model has been widely applied in data science analysis, since it has good performance on predicting data with nonlinearity features. The first topic this thesis interested in is : What is the 1-step-ahead and multi-step-ahead forecast performance using Seasonal ARIMA (SARIMA) in the statistics literature and ANN in the information engineering literature. The second topic is : Is it available to extract or identify the nonlinearity feature from data via ANN fitting procedure? This thesis provides some exploration in this direction. We use simulation data and real data to compare SARIMA and ANN forecast performance. In simulation study, nonlinear random processes are generated with different degrees of nonlinearity with different sample size, and compare SARIMA and ANN forecast performance. We find that ANN model is effectiveness when it applied in time series forecasting. The result shows when sample size is large, ANN has better forecast performance. We can also learn feature of data through observing the ANN hidden layer structure. This thesis advises the appropriate scenarios of using SARIMA and ANN through the analysis results of simulation data and real data.