Title :
A SVCA model for the competition on artificial time series (CATS) benchmark
Author :
Palacios-Gonzalez, Federico
Author_Institution :
Granada Univ., Spain
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;
Conference_Titel :
Neural Networks, 2004. Proceedings. 2004 IEEE International Joint Conference on
Print_ISBN :
0-7803-8359-1
DOI :
10.1109/IJCNN.2004.1381095
