Results 71 to 80 of about 794,789 (182)

Discriminant analysis of multivariate time series using wavelets [PDF]

In analyzing ECG data, the main aim is to differentiate between the signal patterns of those of healthy subjects and those of individuals with specific heart conditions.
Ann Elizabeth Maharaj, M. Andrés Alonso
core  

Prediction of Multivariate Chaotic Time Series using GRU, LSTM and RNN

Sakarya University Journal of Computer and Information Sciences
Chaotic systems are identified as nonlinear, deterministic dynamic systems that are exhibit sensitive to initial values. Some chaotic equations modeled from daily events involve time information and generate chaotic time series that are sequential data ...
Osman Eldoğan, Gülyeter Öztürk
doaj   +1 more source

Specification Testing for Multivariate Time Series Volatility Models [PDF]

Volatility models have been playing an important role in economics and finance. Using a multivariate generalized spectral approach, we propose a new class of generally applicable omnibus tests for univariate and multivariate volatility models. Both GARCH
Yongmiao Hong, Yoon-Jin Lee
core  

Multivariate out-of-sample tests for Granger causality. [PDF]

A time series is said to Granger cause another series if it has incremental predictive power when forecasting it. While Granger causality tests have been studied extensively in the univariate setting, much less is known for the multivariate case. In this
Croux, Christophe, Gelper, Sarah
core  

Nearest Neighbor Multivariate Time Series Forecasting

IEEE Transactions on Neural Networks and Learning Systems
Multivariate time series (MTS) forecasting has a wide range of applications in both industry and academia. Recently, spatial-temporal graph neural networks (STGNNs) have gained popularity as MTS forecasting methods. However, current STGNNs can only use the finite length of MTS input data due to the computational complexity.
Huiliang Zhang, Ping Nie, Lijun Sun, Benoit Boulet   +3 more
openaire   +3 more sources

Deep Learning for Anomaly Detection in Time-Series Data: An Analysis of Techniques, Review of Applications, and Guidelines for Future Research

IEEE Access
Industries are generating massive amounts of data due to increased automation and interconnectedness. As data from various sources becomes more available, the extraction of relevant information becomes crucial for understanding complex systems’ ...
Usman Ahmad Usmani, Izzatdin Abdul Aziz, Jafreezal Jaafar, Junzo Watada   +3 more
doaj   +1 more source

Velocity: a multivariate time-series approach [PDF]

The Federal Reserve announces targets for the monetary aggregates that are implicitly conditioned on an assumption about future velocity for each of the monetary aggregates.
Michael L. Bagshaw, William T. Gavin
core  

Multivariate Bayesian Structural Time Series Model

, 2018
33 ...
Jammalamadaka, S Rao, Qiu, Jinwen, Ning, Ning   +2 more
openaire   +4 more sources

A nonparametric regression cross spectrum for multivariate time series [PDF]

We consider dependence structures in multivariate time series that are characterized by deterministic trends. Results from spectral analysis for stationary processes are extended to deterministic trend functions.
Jan Beran, Mark A. Heiler
core  

Self-Supervised Anomaly Detection Using Outliers for Multivariate Time Series

IEEE Access
Due to the difficulty of having sufficient labeled data, self-supervised learning (SSL) has recently got much attention by many researchers in time series anomaly detection. The generative adversarial network (GAN) based autoencoder model, one of the SSL
Jaehyeop Hong, Youngbum Hur
doaj   +1 more source