DocumentCode
424058
Title
A SVCA model for the competition on artificial time series (CATS) benchmark
Author
Palacios-Gonzalez, Federico
Author_Institution
Granada Univ., Spain
Volume
4
fYear
2004
fDate
25-29 July 2004
Firstpage
2777
Abstract
This paper predicts the 100 missing values in CATS Benchmark. The SVCA model is an autoregressive model in which the coefficients vary smoothly with time. The model is fitted to the first differences of the data by minimising the residual sum of squared, subject certain restrictions that enable the gaps left by the missing observations to be bridged. The path of each time-varying coefficient is described by a combination of a sine and cosine function. The latter are specified via their amplitudes, phases and periods.
Keywords
autoregressive processes; prediction theory; time series; SVCA model; artificial time series benchmark; autoregressive model; time varying coefficient; Artificial neural networks; Autocorrelation; Bars; Cats; Data analysis; Diffusion processes; Electronic mail; Fluctuations; Predictive models;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 2004. Proceedings. 2004 IEEE International Joint Conference on
ISSN
1098-7576
Print_ISBN
0-7803-8359-1
Type
conf
DOI
10.1109/IJCNN.2004.1381095
Filename
1381095
Link To Document