This research project determines which methods are the most effective for finding a best fit model for simultaneous time series. The type of model used was an Autoregressive Integrated Moving Average (ARIMA) model. Two distinct methods were used when determining what order to assign to the ARIMA model: 1.) using the floor of the average number of autoregressive and moving average terms, and 2.) using the ceiling of the average number of autoregressive and moving average terms. After fitting the model, the Akaike Information Criterion (AIC) value for each method measured the goodness of fit to compare to fitting separate models to each series. Based on the results of this research the most effective method depends on the type of data that is being fitted. In most of the different cases explored, the floor function method and the ceiling function method had very similar results. However, for two specific cases the ceiling function was the more effective method. Therefore, it is important to consider the characteristics of the data that is being fitted to determine the most effective method
