On the Stationarity of Multivariate Time Series for Correlation-Based Data Analysis

Yang, Kiyoung; Shahabi, Cyrus

doi:10.1109/icdm.2005.109

Cited by 17 publications

(1 citation statement)

References 27 publications

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“…Wang et al [92] Principal Component Analysis (PCA), as an eigenvalue method, is a technique that transforms the original time-series data into low-dimensional features. As a feature extraction method, PCA is effectively applied to time-series data [109][110][111][112]. It transforms data into a new set of variables whose elements are mutually uncorrelated, thus learning a representation of data that has lower dimensionality than the original input.…”

Section: Features Extractionmentioning

confidence: 99%

Deep Time-Series Clustering: A Review

et al. 2021

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We present a comprehensive, detailed review of time-series data analysis, with emphasis on deep time-series clustering (DTSC), and a case study in the context of movement behavior clustering utilizing the deep clustering method. Specifically, we modified the DCAE architectures to suit time-series data at the time of our prior deep clustering work. Lately, several works have been carried out on deep clustering of time-series data. We also review these works and identify state-of-the-art, as well as present an outlook on this important field of DTSC from five important perspectives.

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Section: Features Extractionmentioning

confidence: 99%

Deep Time-Series Clustering: A Review

et al. 2021

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Review of automated time series forecasting pipelines

Meisenbacher

Turowski

Phipps

et al. 2022

WIREs Data Min & Knowl

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Time series forecasting is fundamental for various use cases in different domains such as energy systems and economics. Creating a forecasting model for a specific use case requires an iterative and complex design process. The typical design process includes five sections (1) data preprocessing, (2) feature engineering, (3) hyperparameter optimization, (4) forecasting method selection, and (5) forecast ensembling, which are commonly organized in a pipeline structure. One promising approach to handle the ever‐growing demand for time series forecasts is automating this design process. The article, thus, reviews existing literature on automated time series forecasting pipelines and analyzes how the design process of forecasting models is currently automated. Thereby, we consider both automated machine learning (AutoML) and automated statistical forecasting methods in a single forecasting pipeline. For this purpose, we first present and compare the identified automation methods for each pipeline section. Second, we analyze these automation methods regarding their interaction, combination, and coverage of the five pipeline sections. For both, we discuss the reviewed literature that contributes toward automating the design process, identify problems, give recommendations, and suggest future research. This review reveals that the majority of the reviewed literature only covers two or three of the five pipeline sections. We conclude that future research has to holistically consider the automation of the forecasting pipeline to enable the large‐scale application of time series forecasting. This article is categorized under: Technologies > Machine Learning Technologies > Prediction Algorithmic Development > Spatial and Temporal Data Mining

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Statistical Analysis of Human Body Movement and Group Interactions in Response to Music

Desmet

Leman²,

Lesaffre³

et al. 2009

Studies in Classification, Data Analysis, and Knowledge Organization

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Summary. Quantification of time series that relate to physiological data is challenging for empirical music research. Up to now, most studies have focused on timedependent responses of individual subjects in controlled environments. However, little is known about time-dependent responses of between-subject interactions in an ecological context. This paper provides new findings on the statistical analysis of group synchronicity in response to musical stimuli. Different statistical techniques were applied to time-dependent data obtained from an experiment on embodied listening in individual and group settings. Analysis of inter group synchronicity are described. Dynamic Time Warping (DTW) and Cross Correlation Function (CCF) were found to be valid methods to estimate group coherence of the resulting movements. It was found that synchronicity of movements between individuals (humanhuman interactions) increases significantly in the social context. Moreover, Analysis of Variance (ANOVA) revealed that the type of music is the predominant factor in both the individual and the social context.

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On the Stationarity of Multivariate Time Series for Correlation-Based Data Analysis

Abstract: Multivariate time series (MTS)

Cited by 17 publications

References 27 publications

Deep Time-Series Clustering: A Review

Deep Time-Series Clustering: A Review

Review of automated time series forecasting pipelines

Statistical Analysis of Human Body Movement and Group Interactions in Response to Music

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