Please use this identifier to cite or link to this item: https://doi.org/10.1002/for.2264
Title: Forecasting simultaneously high-dimensional time series: A robust model-based clustering approach
Authors: Wang, Y.
Tsay, R.S.
Ledolter, J.
Shrestha, K.M. 
Keywords: Hilbert-Huang transform
LASSO regression
Markov chain Monte Carlo
model-based clustering
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
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