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Multivariate Time Series Imputation With Transformers
IEEE Signal Processing Letters, 2022 Processing time series with missing segments is a fundamental challenge that puts obstacles to advanced analysis in various disciplines such as engineering, medicine, and economics. One of the remedies is imputation to fill the missing values based on observed values properly without undermining performance.
A. Yarkın Yldz +2 more
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Frequent State Transition Patterns of Multivariate Time Series
IEEE Access, 2019 Sequence pattern discovery is a key issue in multivariate time series analysis. Popular approaches first obtain the pattern of each single-variate time series and then obtain cross-variate associations.
Zhi-Heng Zhang, Fan Min
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Causality Distance Measures for Multivariate Time Series with Applications
Mathematics, 2021 In this work, we focus on the development of new distance measure algorithms, namely, the Causality Within Groups (CAWG), the Generalized Causality Within Groups (GCAWG) and the Causality Between Groups (CABG), all of which are based on the well-known ...
Achilleas Anastasiou +3 more
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Variance changes detection in multivariate time series [PDF]
, 2004 This paper studies the detection of step changes in the variances and in the correlation structure of the components of a vector of time series. Two procedures are considered.
Daniel Peña +4 more
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High-Dimensional Multivariate Time Series With Additional Structure [PDF]
, 2016 High-dimensional multivariate time series are challenging due to the dependent and high-dimensional nature of the data, but in many applications there is additional structure that can be exploited to reduce computing time along with statistical error. We
Babkin, Sergii +2 more
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Network structure of multivariate time series [PDF]
Scientific Reports, 2015 AbstractOur understanding of a variety of phenomena in physics, biology and economics crucially depends on the analysis of multivariate time series. While a wide range tools and techniques for time series analysis already exist, the increasing availability of massive data structures calls for new approaches for multidimensional signal processing.
Lacasa L +2 more
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Multivariate Time Series Data Prediction Based on ATT-LSTM Network
Applied Sciences, 2021 Deep learning models have been widely used in prediction problems in various scenarios and have shown excellent prediction effects. As a deep learning model, the long short-term memory neural network (LSTM) is potent in predicting time series data ...
Jie Ju, Fang-Ai Liu
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Independence Testing for Multivariate Time Series
, 2020 Complex data structures such as time series are increasingly present in modern data science problems. A fundamental question is whether two such time-series are statistically dependent.
Chung, Jaewon +4 more
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Multivariate time series classification with temporal abstractions [PDF]
, 2009 The increase in the number of complex temporal datasets collected today has prompted the development of methods that extend classical machine learning and data mining methods to time-series data.
Batal, L +3 more
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Time Series Cluster Kernel for Learning Similarities between Multivariate Time Series with Missing Data [PDF]
, 2017 Similarity-based approaches represent a promising direction for time series analysis. However, many such methods rely on parameter tuning, and some have shortcomings if the time series are multivariate (MTS), due to dependencies between attributes, or ...
Bianchi, Filippo Maria +3 more
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