What question did this study set out to answer?

The aim is to create an accurate forecasting model for brain activity using time series analysis.

April 24, 2026Open Access

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

Key Points

The aim is to create an accurate forecasting model for brain activity using time series analysis.
Developed the TBATS forecasting model incorporating trigonometric functions and Box-Cox transformation.
Compared TBATS with naive baseline and traditional methods like Holt-Winters and Seasonal ARIMA.
Focused on individual-specific temporal structures in EEG data.
TBATS showed comparable or superior out-of-sample accuracy to traditional forecasting methods.
It effectively captures subject-specific variations in slow-wave EEG dynamics.
Results support TBATS as a flexible tool for individualized EEG forecasting.

Abstract

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.

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