Quotation Wang, Y., Tsay, R.S., Ledolter, Johannes, Shrestha, K.M. 2013. Forecasting Simultaneously High-Dimensional Time Series: A Robust Model-Based Clustering Approach. Journal of Forecasting 32 (8): 673-684.




This paper considers the problem of forecasting high-dimensional time series. It employs a robust clustering approach to perform classification of the component series. Each series within a cluster is assumed to follow the same model and the data are then pooled for estimation. The classification is model-based and robust to outlier contamination. The robustness is achieved by using the intrinsic mode functions of the Hilbert-Huang transform at lower frequencies. These functions are found to be robust to outlier contamination. The paper also compares out-of-sample forecast performance of the proposed method with several methods available in the literature. The other forecasting methods considered include vector autoregressive models with ∕ without LASSO, group LASSO, principal component regression, and partial least squares. The proposed method is found to perform well in out-of-sample forecasting of the monthly unemployment rates of 50 US states.


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Publication's profile

Status of publication Published
Affiliation WU
Type of publication Journal article
Journal Journal of Forecasting
Citation Index SSCI
WU Journalrating 2009 A
WU-Journal-Rating new FIN-A, INF-A, MAR-B, STRAT-B, VW-D, WH-B
Language English
Title Forecasting Simultaneously High-Dimensional Time Series: A Robust Model-Based Clustering Approach
Volume 32
Number 8
Year 2013
Page from 673
Page to 684
URL http://onlinelibrary.wiley.com/doi/10.1002/for.2264/abstract


Ledolter, Johannes (Former researcher)
Shrestha, K.M
Tsay, R.S.
Wang, Y.
Institute for Statistics and Mathematics IN (Details)
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