Results 31 to 40 of about 794,789 (182)

Detecting nonlinearity in multivariate time series

, 1995
We propose an extension to time series with several simultaneously measured variables of the nonlinearity test, which combines the redundancy -- linear redundancy approach with the surrogate data technique.
Cover, Destexhe, Dvořák, Green, Kaplan, Kennel, Lorenz, Milan Paluš, Morgera, Paluš, Paluš, Paluš, Paluš, Paluš, Paluš, Prichard, Rigney, Savit, Theiler, Tong, Wackermann, Čenys   +21 more
core   +2 more sources

Post Constraint and Correction: A Plug-and-Play Module for Boosting the Performance of Deep Learning Based Weather Multivariate Time Series Forecasting

Applied Sciences
Weather forecasting is essential for various applications such as agriculture and transportation, and relies heavily on meteorological sequential data such as multivariate time series collected from weather stations.
Zhengrui Wang, Zhongwen Luo, Zirui Yang, Yuanyuan Liu   +3 more
doaj   +1 more source

Multivariate time series prediction based on AR_CLSTM

Journal of Measurement Science and Instrumentation, 2021
Time series is a kind of data widely used in various fields such as electricity forecasting, exchange rate forecasting, and solar power generation forecasting, and therefore time series prediction is of great significance.
QIAO Gangzhu, SU Rong, ZHANG Hongfei
doaj  

Scaling analysis of multivariate intermittent time series

, 2005
The scaling properties of the time series of asset prices and trading volumes of stock markets are analysed. It is shown that similarly to the asset prices, the trading volume data obey multi-scaling length-distribution of low-variability periods. In the
Ausloos, Bachmann, Benzi, Bernaola-Galván, Black, Fama, Fama, Fama, Fujiwara, Fujiwara, Gabaix, Gabaix, Gopikrishnan, Jaan Kalda, Kitt, Mandelbrot, Mandelbrot, Mantegna, Mantegna, Markowitz, Maslov, Murphy, Robert Kitt, Sharpe, Shlesinger, Säkki, Vandewalle, Voit, Zipf   +28 more
core   +2 more sources

Data-Adaptive Dynamic Time Warping-Based Multivariate Time Series Fuzzy Clustering

IEEE Access
Multivariate time series (MTS) clustering has become a critical research area. Current methods typically rely on space projection or representation learning for clustering but tend to overlook the significance and contribution of MTS dimensions, leading ...
Qinglin Cai, Leiying Chen, Jian Shao, Ling Chen   +3 more
doaj   +1 more source

Hierarchical Joint Graph Learning and Multivariate Time Series Forecasting

IEEE Access, 2023
Multivariate time series is prevalent in many scientific and industrial domains. Modeling multivariate signals is challenging due to their long-range temporal dependencies and intricate interactions–both direct and indirect.
Juhyeon Kim, Hyungeun Lee, Seungwon Yu, Ung Hwang, Wooyeol Jung, Kijung Yoon   +5 more
doaj   +1 more source

Translating Image XAI to Multivariate Time Series

IEEE Access
As Artificial Intelligence (AI) is becoming part of our daily lives, the need to understand and trust its decisions is becoming a pressing issue. EXplainable AI (XAI) aims at answering this demand, providing tools to get insights into the models’ ...
Lorenzo Tronchin, Ermanno Cordelli, Lorenzo Ricciardi Celsi, Daniele Maccagnola, Massimo Natale, Paolo Soda, Rosa Sicilia   +6 more
doaj   +1 more source

Goodness-of-Fit Tests for Copulas of Multivariate Time Series

Econometrics, 2017
In this paper, we study the asymptotic behavior of the sequential empirical process and the sequential empirical copula process, both constructed from residuals of multivariate stochastic volatility models. Applications for the detection of structural
Bruno Rémillard
doaj   +1 more source

Multivariate Financial Time-Series Prediction With Certified Robustness

IEEE Access, 2020
The futures market's forecasts are significant to investors and policymakers, where the application of deep learning approaches to finance has received a great deal of attention.
Hui Li, Yunpeng Cui, Shuo Wang, Juan Liu, Jinyuan Qin, Yilin Yang   +5 more
doaj   +1 more source

ImputeGAN: Generative Adversarial Network for Multivariate Time Series Imputation

Entropy, 2023
Since missing values in multivariate time series data are inevitable, many researchers have come up with methods to deal with the missing data. These include case deletion methods, statistics-based imputation methods, and machine learning-based ...
Rui Qin, Yong Wang
doaj   +1 more source