Showing papers on "Partial autocorrelation function published in 2001"
TL;DR: In this paper, the authors apply the moving average technique for estimating the seasonal components of time series to monthly tourist arrivals time series data to Australia, using the Akaike Information Criterion and Schwarz Bayesian Criterion to examine which time series processes best describe international arrivals data for Australia.
200 citations
TL;DR: In this article, the authors proposed generalizations based on smoothing techniques, applicable for structural identification of non-linear time series models, and a measure of the departure from linearity is suggested.
39 citations
Posted Content•
TL;DR: In this article, the need to test for the presence of a dependence structure routinely arises in analysing time series of counts, and suitable tests for this purpose are considered in this paper.
Abstract: In analysing time series of counts, the need to test for the presence of a dependence structure routinely arises. Suitable tests for this purpose are considered in this paper.
15 citations
Posted Content•
1 Jan 2001
TL;DR: In this article, the need to test for the presence of a dependence structure routinely arises in analysing time series of counts, and suitable tests for this purpose are considered in this paper.
Abstract: In analysing time series of counts, the need to test for the presence of a dependence structure routinely arises. Suitable tests for this purpose are considered in this paper.
8 citations
Book•
1 Jan 2001
3 citations
Journal Article•
TL;DR: Based on information entropy and mutual information, the definition of nonlinear partial autocorrelation is proposed and by means of it, the quantitative method to measure the intrinsic prediction complexity of time series is got.
Abstract: Based on information entropy and mutual information, we proposed the definition of nonlinear partial autocorrelation. The concept is the generalization of partial autocorrelation. By means of it, we could get the quantitative method to measure the intrinsic prediction complexity of time series. The complexity is determined by the irreducible dependence between current quantities of time series and high order historical quantities, and indicated by the attenuation trend of nonlinear partial autocorrelation. In according to the attenuation trend, in principle, researchers could implement nonlinear model identification, e.g., identification of neural networks. Computer simulations perfectly supported our idea.
1 citations
TL;DR: In this paper, the authors show consistency in the mean integrated quadratic sense of an estimator of the autocorrelation operator in the autoregressive Hilbertian of order one model.