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Estimator’s Properties of Specific Time-Dependent Multivariate Time Series

Mathematics
There is now a vast body of literature on ARMA and VARMA models with time-dependent or time-varying coefficients. A large part of it is based on local stationary processes using time rescaling and assumptions of regularity with respect to time.
Guy Mélard
doaj   +1 more source

KAN-based Unsupervised Multivariate Time Series Anomaly Detection Network [PDF]

Jisuanji kexue
Time series data is widely present in fields such as finance,healthcare,industry,and transportation.Time Series Ano-maly Detection(TSAD) is crucial for ensuring system stability and safety.Most current time series anomaly detection methods are ...
WANG Cheng, JIN Cheng
doaj   +1 more source

DiffTST: Diff Transformer for Multivariate Time Series Forecast

IEEE Access
Deep learning models employing the Transformer architecture have demonstrated exceptional performance in the field of multivariate time series forecasting research.
Song Yang, Wenyong Han, Yaping Wan, Tao Zhu, Zhiming Liu, Shuangjian Li   +5 more
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Dynamic Covariance Models for Multivariate Financial Time Series [PDF]

, 2013
The accurate prediction of time-changing covariances is an important problem in the modeling of multivariate financial data. However, some of the most popular models suffer from a) overfitting problems and multiple local optima, b) failure to capture ...
Ghahramani, Zoubin, Hernández-Lobato, José Miguel, Wu, Yue   +2 more
core  

Outlier detection in multivariate time series via projection pursuit [PDF]

, 2004
This article uses Projection Pursuit methods to develop a procedure for detecting outliers in a multivariate time series. We show that testing for outliers in some projection directions could be more powerful than testing the multivariate series directly.
Galeano, Pedro, Peña, Daniel, Tsay, Ruey S.   +2 more
core   +1 more source

On nonparametric and semiparametric testing for multivariate linear time series

, 2009
We formulate nonparametric and semiparametric hypothesis testing of multivariate stationary linear time series in a unified fashion and propose new test statistics based on estimators of the spectral density matrix.
Matsuda, Yasumasa, Yajima, Yoshihiro
core   +1 more source

Spectral tail processes and max-stable approximations of multivariate regularly varying time series

, 2018
A regularly varying time series as introduced in Basrak and Segers (2009) is a (multivariate) time series such that all finite dimensional distributions are multivariate regularly varying.
Janßen, Anja
core   +1 more source

Multivariate LSTM-FCNs for time series classification [PDF]

Neural Networks, 2019
Over the past decade, multivariate time series classification has received great attention. We propose transforming the existing univariate time series classification models, the Long Short Term Memory Fully Convolutional Network (LSTM-FCN) and Attention LSTM-FCN (ALSTM-FCN), into a multivariate time series classification model by augmenting the fully ...
Karim, Fazle, Majumdar, Somshubra, Darabi, Houshang, Harford, Samuel   +3 more
openaire   +3 more sources

ADTime: Adaptive Multivariate Time Series Forecasting Using LLMs

Machine Learning and Knowledge Extraction
Large language models (LLMs) have recently demonstrated notable performance, particularly in addressing the challenge of extensive data requirements when training traditional forecasting models.
Jinglei Pei, Yang Zhang, Ting Liu, Jingbin Yang, Qinghua Wu, Kang Qin   +5 more
doaj   +1 more source

Data Augmentation with Suboptimal Warping for Time-Series Classification

Sensors, 2019
In this paper, a novel data augmentation method for time-series classification is proposed. In the introduced method, a new time-series is obtained in warped space between suboptimally aligned input examples of different lengths.
Krzysztof Kamycki, Tomasz Kapuscinski, Mariusz Oszust   +2 more
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