PROJECT DESCRIPTION: Utilizing time series analysis and forecasting methodologies to predict eye states that serve as reliable indicators of alertness. By accurately identifying these states, I aim to enhance workplace safety protocols and reduce accident risks.

OBJECTIVES: My goal is to create an effective system for monitoring alertness in real-time by utilizing EEG data. I set to develop predictive models with exceptional accuracy by employing time series forecasting techniques.

METHODOLOGY: The project will employ moving averages, exponential smoothing, advanced exponential smoothing, multiple regression, auto regression and ARIMA models, processed through R, focusing on predictive accuracy and real-world applicability.

EXPECTED OUTCOMES: A deployable system capable of real-time alertness detection, significantly improving safety measures in critical work environments.

RESOURCES: EEG Eye State dataset, R software, and time series analysis libraries like forecast, tseries.

CONCLUSION: By accurately predicting alertness levels, this project aims to introduce a proactive approach to enhancing safety and efficiency in workplaces.