用Transformer和集成学习预测睡眠质量与情绪状态
TraM : Enhancing User Sleep Prediction with Transformer-based Multivariate Time Series Modeling and Machine Learning Ensembles
- 结合时间序列Transformer与机器学习集成模型
- 在实验中取得6.10分（满分10分）的预测性能
- 适合关注睡眠分析与心理健康监测的研究者
本文提出一种新方法，利用基于Transformer的多变量时间序列模型与机器学习集成技术，预测人类睡眠质量、情绪状态及压力水平。研究设计了一种标签计算公式，并将不同模型应用于用户数据：对于具有时间序列特性的标签，采用时间序列Transformer；对于需综合每日活动统计的标签，则使用机器学习集成模型。时间序列Transformer通过预训练捕捉时序特征，而机器学习集成模型则根据分类标准选取最优模型组合。实验结果表明，所提出的TraM模型在测试中获得6.10分（满分10分），优于其他方法。代码与配置已公开于https://github.com/jin-jae/ETRI-Paper-Contest。
原文摘要 · Abstract (English)
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.
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