Title of article :
More effective time-series analysis and forecasting
Author/Authors :
Anderson، نويسنده , , Oliver D.، نويسنده ,
Issue Information :
روزنامه با شماره پیاپی سال 1995
Abstract :
Our aim is to suggest ways of improving time-domain modelling, for the purpose of more effective forecasting, by better interpretation of the sample autocorrelations and partial autocorrelations obtained from raw time-series data. For this objective, we assume no specialist knowledge, as we start by surveying all those standard ideas of univariate analysis which are needed for the subsequent development of our thesis.
Keywords :
Process modelling , ARIMA and ARUMA processes , ARMA , serial correlation , Time domain , Partial autocorrelation , Identifying nonstationarity
Journal title :
Journal of Computational and Applied Mathematics
Journal title :
Journal of Computational and Applied Mathematics