Exploiting Individual Graph Structures to Enhance Ecological Momentary
  Assessment (EMA) Forecasting
Mandani Ntekouli Gerasimos Spanakis Lourens Waldorp Anne Roefs
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.