Change-point methods for multivariate time-series: paired vectorial observations
Sign Up to like & getrecommendations! Published in 2020 at "Statistical Papers"
DOI: 10.1007/s00362-020-01175-3
Abstract: We consider paired and two-sample break-detection procedures for vectorial observations and multivariate time series. The new methods involve L2-type criteria based on empirical characteristic functions and are easy to compute regardless of dimension. We obtain… read more here.
Keywords: time series; change point; time; series ... See more keywords
Ratai: recurrent autoencoder with imputation units and temporal attention for multivariate time series imputation
Sign Up to like & getrecommendations! Published in 2024 at "Artificial Intelligence Review"
DOI: 10.1007/s10462-024-11039-z
Abstract: Multivariate time series is ubiquitous in real-world applications, yet it often suffers from missing values that impede downstream analytical tasks. In this paper, we introduce the Long Short-Term Memory Network based Recurrent Autoencoder with Imputation… read more here.
Keywords: multivariate time; time; imputation; time series ... See more keywords
Universal representation learning for multivariate time series using the instance-level and cluster-level supervised contrastive learning
Sign Up to like & getrecommendations! Published in 2024 at "Data mining and knowledge discovery"
DOI: 10.1007/s10618-024-01006-1
Abstract: The multivariate time series classification (MTSC) task aims to predict a class label for a given time series. Recently, modern deep learning-based approaches have achieved promising performance over traditional methods for MTSC tasks. The success… read more here.
Keywords: multivariate time; time; instance; level ... See more keywords
Multivariate multiscale increment entropy: a complexity measure for detecting flow pattern transition in multiphase flows
Sign Up to like & getrecommendations! Published in 2020 at "Nonlinear Dynamics"
DOI: 10.1007/s11071-020-05733-0
Abstract: Multivariate time series are routinely measured in real complex systems, and effective multivariate multiscale methods for uncovering the complexity of multivariate complex systems are certainly needed. In this paper, a new complexity measure for multiscale… read more here.
Keywords: multivariate; time series; multivariate time; complexity ... See more keywords
Tri-Partition State Alphabet-Based Sequential Pattern for Multivariate Time Series
Sign Up to like & getrecommendations! Published in 2021 at "Cognitive Computation"
DOI: 10.1007/s12559-021-09871-4
Abstract: Recently, the advancement of cognitive computing and three-way decisions has enabled in-depth sequential pattern understanding through temporal association analysis. The main challenge is to obtain concise patterns that express richer semantics for multivariate time series… read more here.
Keywords: tri partition; multivariate time; tri; sequential pattern ... See more keywords
BiLSTM model based on multivariate time series data in multiple field for forecasting trading area
Sign Up to like & getrecommendations! Published in 2019 at "Journal of Ambient Intelligence and Humanized Computing"
DOI: 10.1007/s12652-019-01398-9
Abstract: An artificial neural network-based model is widely used for analyzing and predicting multivariate time series data. However, the study on the analysis and prediction of multivariate time series data in multiple fields has limitations in… read more here.
Keywords: series data; time; time series; model ... See more keywords
Stationary vine copula models for multivariate time series
Sign Up to like & getrecommendations! Published in 2022 at "Journal of Econometrics"
DOI: 10.1016/j.jeconom.2021.11.015
Abstract: Multivariate time series exhibit two types of dependence: across variables and across time points. Vine copulas are graphical models for the dependence and can conveniently capture both types of dependence in the same model. We… read more here.
Keywords: stationary vine; time; time series; multivariate time ... See more keywords
A multivariate time series prediction model based on the KAN network
Sign Up to like & getrecommendations! Published in 2025 at "Scientific Reports"
DOI: 10.1038/s41598-025-07654-7
Abstract: Time series forecasting is crucial in various fields such as financial markets and weather prediction. Although mainstream deep learning models like RNNs and CNNs have made some progress in capturing short-term patterns, they still fall… read more here.
Keywords: multivariate time; time; prediction; model ... See more keywords
Complexity of couplings in multivariate time series via ordinal persistent homology.
Sign Up to like & getrecommendations! Published in 2023 at "Chaos"
DOI: 10.1063/5.0136772
Abstract: We propose a new measure of the complexity of couplings in multivariate time series by combining the techniques of ordinal pattern analysis and topological data analysis. We construct an increasing sequence of simplicial complexes encoding… read more here.
Keywords: complexity couplings; multivariate time; complexity; time series ... See more keywords
A sliding window-based multi-stage clustering and probabilistic forecasting approach for large multivariate time series data
Sign Up to like & getrecommendations! Published in 2017 at "Journal of Statistical Computation and Simulation"
DOI: 10.1080/00949655.2017.1299151
Abstract: ABSTRACT Time series data analysis, such as temporal pattern recognition and trend forecasting, plays an increasingly significant part in temporal data statistics and analytics. Yet challenges still exist in the efficiency of pattern extracting and… read more here.
Keywords: stage clustering; multivariate time; time; time series ... See more keywords
Causality enhanced deep learning framework for quality characteristic prediction via long sequence multivariate time-series data
Sign Up to like & getrecommendations! Published in 2025 at "Measurement Science and Technology"
DOI: 10.1088/1361-6501/adb05a
Abstract: Prediction of product quality characteristics plays an important role in the timely identification of quality conditions and in triggering an alarm for abnormal products. In modern manufacturing, the large number of parameters collected by sensors… read more here.
Keywords: quality; multivariate time; time; prediction ... See more keywords