More effective time-series analysis and forecasting
type: Item
instance of: scholarly article
title: More effective time-series analysis and forecasting
zbMATH Open document ID: 0847.62073
DOI: 10.1016/0377-0427(95)00011-9
author: Oliver D. Anderson
published in: Journal of Computational and Applied Mathematics
publication date: 1996-10-08T00:00:00Z
Mathematics Subject Classification ID: 62M10
Mathematics Subject Classification ID: 62M20
zbMATH DE Number: 903542
zbMATH Keywords: ARMA
zbMATH Keywords: ARIMA
zbMATH Keywords: ARUMA processes
zbMATH Keywords: identifying nonstationarity
zbMATH Keywords: process modelling
zbMATH Keywords: serial correlation
zbMATH Keywords: nonstationary models
zbMATH Keywords: improving time-domain modelling
zbMATH Keywords: forecasting
zbMATH Keywords: sample autocorrelations
zbMATH Keywords: partial autocorrelations
zbMATH Keywords: time-series
MaRDI profile type: Publication
full work available at URL: 0377-0427(95)00011-9
OpenAlex ID: W2027582252
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cites work: Small-sample Autocorrelation Structure for Long-memory Time Series
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cites work: Q3316428
cites work: Sampled autocovariance and autocorrelation results for linear time processes
cites work: Discrimination between nonstationary and nearly nonstationary processes, and its effect on forecasting
cites work: Discriminating between nonstationary and nearly nonstationary time series models: A simulation study
cites work: Q3847819
cites work: NOTE ON BIAS IN THE ESTIMATION OF AUTOCORRELATION
cites work: Q3963838
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cites work: The behaviour of the sample autocorrelation function for an integrated moving average process
Wikidata QID: Q126297114
description: scientific article; zbMATH DE number 903542