Learning from Time-Tracking Data: Gaussian Process Regression for Efficient Time Management Systems

This paper aims at analyzing the use of Gaussian Process Regression (GPR) in working and creating effective time management system. It incorporates the GPR model that helps in predicting the future time usage by dissecting the time-study data this assists the users in scheduling. The approach adopted entails harvesting data from automated tools and manual logs, pre-processing the data collected, using k-fold cross-validation in training and validating the model. The forecasting accuracy features a low Mean Absolute Error and a high R-squared value, which signifies that the models perform well in terms of predictive capability. This research proves that GPR can actively help with data on time management and benefit time management systems as a whole. Some of the challenges that faced the research include quality of the data and the computation required to process the data was enhanced by effective preprocessing and refined algorithms respectively.