The article introduces a new method called MAICE for identifying statistical models in time series analysis. This method aims to improve upon traditional hypothesis testing by using a minimum information theoretical criterion. By comparing different models and their maximum likelihood estimates, MAICE helps to select the best model without the uncertainties of conventional methods. The practical examples provided show that MAICE is a useful tool for analyzing time series data.