# Clustering individuals based on multivariate EMA time-series data

arXiv: https://arxiv.org/abs/2212.01159 · HTML: https://mlsanity.com/paper/2212.01159/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 20 references that are in the map.
- First posted on arXiv 2 Dec 2022.

## Builds on (most cited first, 4 of 4)

- [Soft-DTW: a Differentiable Loss Function for Time-Series](https://mlsanity.com/paper/1703.01541/) (2017)
- [A Benchmark Study on Time Series Clustering](https://mlsanity.com/paper/2004.09546/) (2020)
- [Time Series Cluster Kernel for Learning Similarities between Multivariate Time Series with Missing Data](https://mlsanity.com/paper/1704.00794/) (2017)
- [Using Explainable Boosting Machine to Compare Idiographic and Nomothetic Approaches for Ecological Momentary Assessment Data](https://mlsanity.com/paper/2204.01689/) (2022)

## Built on by (most cited first, 1 of 1)

- [Model-based Clustering of Individuals' Ecological Momentary Assessment Time-series Data for Improving Forecasting Performance](https://mlsanity.com/paper/2310.07491/) (2023)

## Abstract (arXiv)

In the field of psychopathology, Ecological Momentary Assessment (EMA) methodological advancements have offered new opportunities to collect time-intensive, repeated and intra-individual measurements. This way, a large amount of data has become available, providing the means for further exploring mental disorders. Consequently, advanced machine learning (ML) methods are needed to understand data characteristics and uncover hidden and meaningful relationships regarding the underlying complex psychological processes. Among other uses, ML facilitates the identification of similar patterns in data of different individuals through clustering. This paper focuses on clustering multivariate time-series (MTS) data of individuals into several groups. Since clustering is an unsupervised problem, it is challenging to assess whether the resulting grouping is successful. Thus, we investigate different clustering methods based on different distance measures and assess them for the stability and quality of the derived clusters. These clustering steps are illustrated on a real-world EMA dataset, including 33 individuals and 15 variables. Through evaluation, the results of kernel-based clustering methods appear promising to identify meaningful groups in the data. So, efficient representations of EMA data play an important role in clustering.