A sliding window-based multi-stage clustering and probabilistic forecasting approach for large multivariate time series data-Reference-Cited by-同舟云学术

A sliding window-based multi-stage clustering and probabilistic forecasting approach for large multivariate time series data

Published:2017-03-05 Issue:13 Volume:87 Page:2494-2508
ISSN:0094-9655
Container-title:Journal of Statistical Computation and Simulation
language:en
Short-container-title:Journal of Statistical Computation and Simulation

Author:

Ren Lei¹²,Wei Yongchang¹,Cui Jin¹,Du Yi³

Affiliation:

1. School of Automation Science and Electrical Engineering, Beihang University, Beijing, People's Republic of China

2. Engineering Research Center of Complex Product Advanced Manufacturing System, Ministry of Education, Beijing, People's Republic of China

3. Department of Big Data Technology and Application Development, Computer Network Information Center, Chinese Academy of Sciences, Beijing, People's Republic of China

Funder

National Science Foundation of China

National High-Tech Research and Development Plan of China

Publisher

Informa UK Limited

Subject

Applied Mathematics,Statistics, Probability and Uncertainty,Modelling and Simulation,Statistics and Probability

Link

https://www.tandfonline.com/doi/pdf/10.1080/00949655.2017.1299151

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2. Tests for serial correlation in mean and variance of a sequence of time series objects

3. Empirical measures of signed serial dependence in categorical time series

4. Dynamic changepoint detection in count time series: a particle filter approach

5. A New Time Series Mining Approach Applied to Multitemporal Remote Sensing Imagery

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