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Generalized discrete autoregressive moving‐average models
Author(s) -
Möller Tobias A.,
Weiß Christian H.
Publication year - 2020
Publication title -
applied stochastic models in business and industry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.413
H-Index - 40
eISSN - 1526-4025
pISSN - 1524-1904
DOI - 10.1002/asmb.2520
Subject(s) - autoregressive model , autocorrelation , univariate , series (stratigraphy) , autoregressive integrated moving average , autoregressive–moving average model , moving average model , moving average , partial autocorrelation function , time series , star model , mathematics , multivariate statistics , econometrics , computer science , statistics , paleontology , biology
This article proposes the generalized discrete autoregressive moving‐average (GDARMA) model as a parsimonious and universally applicable approach for stationary univariate or multivariate time series. The GDARMA model can be applied to any type of quantitative time series. It allows to compute moment properties in a unique way, and it exhibits the autocorrelation structure of the traditional ARMA model. This great flexibility is obtained by using data‐specific variation operators, which is illustrated for the most common types of time series data, such as counts, integers, reals, and compositional data. The practical potential of the GDARMA approach is demonstrated by considering a time series of integers regarding votes for a change of the interest rate, and a time series of compositional data regarding television market shares.