Results 121 to 130 of about 794,789 (182)

Sparse transformer with local and seasonal adaptation for multivariate time series forecasting. [PDF]

Sci Rep
Zhang Y, Wu R, Dascalu SM, Harris FC.
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A deep learning identification method of tight sandstone lithofacies integrating multilayer perceptron and multivariate time series. [PDF]

Sci Rep
Mu Z, Li C, Liu Z, Liu T, Zhang K, Mu H, Yang Y, Xu C, Zhang R.   +8 more
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A dynamic customer segmentation approach by combining LRFMS and multivariate time series clustering. [PDF]

Sci Rep
Wang S, Sun L, Yu Y.
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A novel extreme adaptive GRU for multivariate time series forecasting. [PDF]

Sci Rep
Zhang Y, Wu R, Dascalu SM, Harris FC.
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A novel method to select time-varying multivariate time series models for the surveillance of infectious diseases. [PDF]

BMC Infect Dis
Yu J, Wang H, Chen M, Han X, Deng Q, Yang C, Zhu W, Ma Y, Yin F, Weng Y, Yang C, Zhang T.   +11 more
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General value functions for fault detection in multivariate time series data. [PDF]

Front Robot AI
Wong A, Taghian Jazi M, Takeuchi T, Günther J, Zaïane O.   +4 more
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Anomaly detection in multivariate time series data using deep ensemble models.

PLoS One
Iqbal A, Amin R, Alsubaei FS, Alzahrani A.   +3 more
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Multivariate Time Series

2017
In this chapter vector time series models are considered for stationary processes. There is a brief discussion of stationarity, but we leave the reader to refer for further detail to Patterson (2010) and (2011). The models are decomposed into VAR, VMA and mixed models with both characteristics (VARMA).
John Hunter, Simon P. Burke, Alessandra Canepa   +2 more
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Multivariate Time Series

1987
Many time series arising in practice are best considered as components of some vector-valued (multivariate) time series {X t } whose specification includes not only the serial dependence of each component series {X tj } but also the interdependence between different component series {X ti } and {X tj }.
Peter J. Brockwell, Richard A. Davis
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Modeling multivariate time series

2023
This thesis deals with the construction of multivariate Integer Generalized Au toregressive Conditional Heteroskedastic(INGARCH) and Conditional Autoregres sive Range(CARR) time series processes. The INGARCH(1,1) model has been alsostudied in multivariate case and implementations in 2 dimensions have been also pre sented recent years.
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