In the evolving field of psychopathology, the accurate assessment and
forecasting of data derived from Ecological Momentary Assessment (EMA) is
crucial. EMA offers contextually-rich psychopathological measurements over
time, that practically lead to Multivariate Time Series (MTS) data. Thus, many
challenges arise in analysis from the temporal complexities inherent in
emotional, behavioral, and contextual EMA data as well as their
inter-dependencies. To address both of these aspects, this research
investigates the performance of Recurrent and Temporal Graph Neural Networks
(GNNs). Overall, GNNs, by incorporating additional information from graphs
reflecting the inner relationships between the variables, notably enhance the
results by decreasing the Mean Squared Error (MSE) to 0.84 compared to the
baseline LSTM model at 1.02. Therefore, the effect of constructing graphs with
different characteristics on GNN performance is also explored. Additionally,
GNN-learned graphs, which are dynamically refined during the training process,
were evaluated. Using such graphs showed a similarly good performance. Thus,
graph learning proved also promising for other GNN methods, potentially
refining the pre-defined graphs.

该研究调查了借助回归和时间图神经网络（GNNs）评估和预测生态瞬时评估（EMA）数据的性能，发现 GNNs 能够在增加内部变量关系的图的情况下显著提升结果，将均方误差（MSE）从基准 LSTM 模型的 1.02 降低至 0.84；此外，也探讨了构建具有不同特征的图对 GNN 性能的影响，并发现使用动态优化的 GNN 学习图具有类似良好的性能，因此图学习对其他 GNN 方法也具有潜在的改进作用。