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|Title:||Forecasting simultaneously high-dimensional time series: A robust model-based clustering approach||Authors:||Wang, Y.
Markov chain Monte Carlo
partial least squares
principal component regression
|Issue Date:||Dec-2013||Citation:||Wang, Y., Tsay, R.S., Ledolter, J., Shrestha, K.M. (2013-12). Forecasting simultaneously high-dimensional time series: A robust model-based clustering approach. Journal of Forecasting 32 (8) : 673-684. ScholarBank@NUS Repository. https://doi.org/10.1002/for.2264||Abstract:||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 â̂ 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. Copyright © 2013 John Wiley & Sons, Ltd. Copyright © 2013 John Wiley & Sons, Ltd.||Source Title:||Journal of Forecasting||URI:||http://scholarbank.nus.edu.sg/handle/10635/128735||ISSN:||02776693||DOI:||10.1002/for.2264|
|Appears in Collections:||Staff Publications|
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