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In this research, we explore a categorical time series data that changes with time and other input variables using a combination of Logistic Regression, and ARIMA model. We use an Electroencephalogram (EEG) dataset with two states of the response variable (closed or open state of the eye). Using EEG sensor values as input, we use Logistic Regression to obtain the predictive probability to classify the eye state. Due to the autocorrelation among the residuals and to time dependence, the Logistic Regression model can be improved using ARIMA to produce better results. This will help making the residuals a white noise. This work is developed further using a Transfer Function model that produce an even more better result.
Primary Advisor's Department
Stander Symposium, College of Arts and Sciences
"Forecasting Categorical Time Series Using Logistic Regression and ARIMA model" (2023). Stander Symposium Projects. 3173.