TraM : Enhancing User Sleep Prediction with Transformer-based Multivariate Time Series Modeling and Machine Learning Ensembles
This work addresses the problem of personalized health monitoring for users by providing an incremental improvement in prediction accuracy for sleep and emotional metrics.
This paper tackled the problem of predicting human sleep quality, emotional states, and stress levels by developing a novel approach called TraM, which combines Transformer-based multivariate time series modeling and Machine Learning Ensembles, achieving a score of 6.10 out of 10 in experiments.
This paper presents a novel approach that leverages Transformer-based multivariate time series model and Machine Learning Ensembles to predict the quality of human sleep, emotional states, and stress levels. A formula to calculate the labels was developed, and the various models were applied to user data. Time Series Transformer was used for labels where time series characteristics are crucial, while Machine Learning Ensembles were employed for labels requiring comprehensive daily activity statistics. Time Series Transformer excels in capturing the characteristics of time series through pre-training, while Machine Learning Ensembles select machine learning models that meet our categorization criteria. The proposed model, TraM, scored 6.10 out of 10 in experiments, demonstrating superior performance compared to other methodologies. The code and configuration for the TraM framework are available at: https://github.com/jin-jae/ETRI-Paper-Contest.