Temporal State-space model for forecasting slow-wave EEG power in non-human primates

Being able to accurately forecast brain activity over a prolonged period of time can help us establish a baseline of neural dynamics before external intervention. In this study, we developed an individualized time series forecasting framework based on a Trigonometric, Box-Cox transformation with ARMA errors and Trend and Seasonal/Periodic Components (TBATS), to predict slow-wave activity (SWA) power in non-human primates. Compared to a naive baseline and traditional methods such as Holt-Winters (HW) and Seasonal ARIMA (SARIMA), TBATS demonstrated comparable and even better out-of-sample accuracy while offering the flexibility to capture subject-specific latent temporal structure. These results support the use of TBATS as a data-efficient, interpretable tool for individualized forecasting of longitudinal EEG dynamics.