Paper ID: 2410.11293 • Published Oct 15, 2024
TraM : Enhancing User Sleep Prediction with Transformer-based Multivariate Time Series Modeling and Machine Learning Ensembles
Jinjae Kim, Minjeong Ma, Eunjee Choi, Keunhee Cho, Chanwoo Lee
TL;DR
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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: this https URL