mlsanityData to 7 Oct 2026

Exploiting Individual Graph Structures to Enhance Ecological Momentary Assessment (EMA) Forecasting

arXiv 2403.19442 ↗First posted 28 Mar 2024cs.LGcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 14 of its 28 references that are in the map. First posted on arXiv 28 Mar 2024.

Builds on

14 of 28 references.

References · 14

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2 more
  1. 2022Using Explainable Boosting Machine to Compare Idiographic and Nomothetic Approaches for Ecological Momentary Assessment Datacited by 4
  2. 2023A Comparative Study of Population-Graph Construction Methods and Graph Neural Networks for Brain Age Regressioncited by 2

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Citing papers per year: 2024: 1.

1 paper.

  1. 2024Graph Retention Networks for Dynamic Graphscited by 2