Elastic Similarity and Distance Measures for Multivariate Time Series

Shifaz, Ahmed; Pelletier, Charlotte; Petitjean, François; Webb, Geoffrey I.

doi:10.48550/arxiv.2102.10231

Cited by 3 publications

(2 citation statements)

References 23 publications

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“…Consequently, we represent each speaker behaviour by combining all frame-level descriptors as a multi-channel audio-visual time-series behavioural signal. We then apply elastic similarity measurement (i.e., DTW for multi-channel time-series) [20] to compute the similarity between a pair of multi-channel time-series speaker behaviours as:…”

Section: Automatic Appropriate Facial Reaction Labelling Strategymentioning

confidence: 99%

Multiple Appropriate Facial Reaction Generation in Dyadic Interaction Settings: What, Why and How?

Song¹,

Spitale²,

Luo³

et al. 2023

Preprint

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According to the Stimulus Organism Response (SOR) theory, all human behavioral reactions are stimulated by context, where people will process the received stimulus and produce an appropriate reaction. This implies that in a specific context for a given input stimulus, a person can react differently according to their internal state and other contextual factors. Analogously, in dyadic interactions, humans communicate using verbal and nonverbal cues, where a broad spectrum of listeners' nonverbal reactions might be appropriate for responding to a specific speaker behaviour. There already exists a body of work that investigated the problem of automatically generating an appropriate reaction for a given input. However, none attempted to automatically generate multiple appropriate reactions in the context of dyadic interactions and evaluate the appropriateness of those reactions using objective measures. This paper starts by defining the facial Multiple Appropriate Reaction Generation (fMARG) task for the first time in the literature and proposes a new set of objective evaluation metrics to evaluate the appropriateness of the generated reactions. The paper subsequently introduces a framework to predict, generate, and evaluate multiple appropriate facial reactions.

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Section: Automatic Appropriate Facial Reaction Labelling Strategymentioning

confidence: 99%

Multiple Appropriate Facial Reaction Generation in Dyadic Interaction Settings: What, Why and How?

Song¹,

Spitale²,

Luo³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…In the univariate case, this comes down to d(x, y) = (x − y) 2 . DTW D allows to "warp" across the channels of X and Y by simply using the generalization to the k-dimensional space d(x, y) = ||x − y|| Shifaz et al (2021)). Brophy (2020) used the measure for evaluating a GAN producing multivariate time series.…”

Section: Sample-level Measuresmentioning

confidence: 99%

Evaluation is Key: A Survey on Evaluation Measures for Synthetic Time Series

Stenger,

Leppich,

Foster

et al. 2023

Preprint

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Synthetic data generation describes the process of learning the underlying distribution of a given real dataset in a model, which is, in turn, sampled to produce new data objects still adhering to the original distribution. This approach often finds application where circumstances limit the availability or usability of real-world datasets, for instance, in health care due to privacy concerns. While image synthesis has received much attention in the past, time series are arguably even more relevant for many practical (e.g., industrial) applications. By now, numerous different generative models and measures to evaluate time series syntheses have been proposed. However, when it comes to what characterizes high-quality synthetic time series and how to quantify quality, no consensus has yet been reached among researchers. Hence, we propose this comprehensive survey on evaluation measures for time series generation to assist users in evaluating synthetic time series. We provide brief descriptions or - where applicable - precise definitions and also a multidimensional analysis of their properties, applicability, and usage. In order to facilitate the selection of the most suitable measures, we provide a quick guide combined with many tables and figures. Notably, during our study, we found that there is currently no generally accepted approach for an evaluation procedure, including what measures to use. We believe this situation hinders progress and may even erode evaluation standards to a ``do as you like''-approach to synthetic data evaluation. Therefore, this survey is a preliminary step to advance the field of synthetic data evaluation.

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Thinking in Categories: A Survey on Assessing the Quality for Time Series Synthesis

Stenger,

Bauer,

Prantl

et al. 2024

J. Data and Information Quality

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Time series data are widely used and provide a wealth of information for countless applications. However, some applications are faced with a limited amount of data, or the data cannot be used due to confidentiality concerns. To overcome these obstacles, time series can be generated synthetically. For example, electrocardiograms can be synthesized to make them available for building models to predict conditions such as cardiac arrhythmia without leaking patient information. Although many different approaches to time series synthesis have been proposed, evaluating the quality of synthetic time series data poses unique challenges and remains an open problem, as there is a lack of a clear definition of what constitutes a “good” synthesis. To this end, we present a comprehensive literature survey to identify different aspects of synthesis quality and their relationships. Based on this, we propose a definition of synthesis quality and a systematic evaluation procedure for assessing it. With this work, we aim to provide a common language and criteria for evaluating synthetic time series data. Our goal is to promote more rigorous and reproducible research in time series synthesis by enabling researchers and practitioners to generate high-quality synthetic time series data.

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Elastic Similarity and Distance Measures for Multivariate Time Series

Cited by 3 publications

References 23 publications

Multiple Appropriate Facial Reaction Generation in Dyadic Interaction Settings: What, Why and How?

Multiple Appropriate Facial Reaction Generation in Dyadic Interaction Settings: What, Why and How?

Evaluation is Key: A Survey on Evaluation Measures for Synthetic Time Series

Thinking in Categories: A Survey on Assessing the Quality for Time Series Synthesis

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