Journal article
Forecasting Simultaneously High-Dimensional Time Series: A Robust Model-Based Clustering Approach
Journal of forecasting, Vol.32(8), pp.673-684
12/2013
DOI: 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/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 (c) 2013 John Wiley & Sons, Ltd.
Details
- Title: Subtitle
- Forecasting Simultaneously High-Dimensional Time Series: A Robust Model-Based Clustering Approach
- Creators
- Yongning Wang - University of ChicagoRuey S. Tsay - University of ChicagoJohannes Ledolter - University of IowaKeshab M. Shrestha - National University of Singapore
- Resource Type
- Journal article
- Publication Details
- Journal of forecasting, Vol.32(8), pp.673-684
- Publisher
- Wiley
- DOI
- 10.1002/for.2264
- ISSN
- 0277-6693
- eISSN
- 1099-131X
- Number of pages
- 12
- Language
- English
- Date published
- 12/2013
- Academic Unit
- Statistics and Actuarial Science; Business Analytics
- Record Identifier
- 9984380395002771
Metrics
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