TraM : Enhancing User Sleep Prediction with Transformer-based Multivariate Time Series Modeling and Machine Learning Ensembles

TL;DR

This paper introduces TraM, a hybrid model combining Transformer-based multivariate time series analysis and machine learning ensembles to predict sleep quality, emotional states, and stress levels with improved accuracy.

Contribution

The paper proposes a novel hybrid framework that integrates Transformer models and machine learning ensembles for multi-faceted human state prediction.

Findings

TraM outperforms existing methods with a score of 6.10/10.

Transformer models effectively capture time series features for sleep and stress prediction.

Machine learning ensembles enhance prediction accuracy for daily activity-based labels.

Abstract

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.