2011 NonparametricTimeSeriesForecast

(Shang & Hyndman, 2011) ⇒ Han Lin Shang, and Rob J. Hyndman. (2011). “Nonparametric Time Series Forecasting with Dynamic Updating.” In: Mathematics and Computers in Simulation Journal, 81(7). doi:10.1016/j.matcom.2010.04.027

Subject Headings: Nonparametric Model, Time Series Forecasting.

Notes

Presentation at: http://forecasters.org/submissions09/ShangHanLinISF2009.pdf.pdf

Cited By

Quotes

Author Keywords

Functional principal component analysis; Functional time series; Penalized least squares; Ridge regression; Seasonal time series

Abstract

We present a nonparametric method to forecast a seasonal univariate time series, and propose four dynamic updating methods to improve point forecast accuracy. Our methods consider a seasonal univariate time series as a functional time series. We propose first to reduce the dimensionality by applying functional principal component analysis to the historical observations, and then to use univariate time series forecasting and functional principal component regression techniques. When data in the most recent year are partially observed, we improve point forecast accuracy by using dynamic updating methods. We also introduce a nonparametric approach to construct prediction intervals of updated forecasts, and compare the empirical coverage probability with an existing parametric method. Our approaches are data-driven and computationally fast, and hence they are feasible to be applied in real time high frequency dynamic updating. The methods are demonstrated using monthly sea surface temperatures from 1950 to 2008.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2011 NonparametricTimeSeriesForecast	Han Lin Shang Rob J. Hyndman			Nonparametric Time Series Forecasting with Dynamic Updating				10.1016/j.matcom.2010.04.027