Nonparametric time series forecasting with dynamic updating
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...[Show more]
|Collections||ANU Research Publications|
|Source:||Mathematics and Computers in Simulation|
|01_Shang_Nonparametric_time_series_2011.pdf||556.53 kB||Adobe PDF||Request a copy|
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