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A unified view of statistical forecasting procedures
Author(s) -
Harvey A. C.
Publication year - 1984
Publication title -
journal of forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.543
H-Index - 59
eISSN - 1099-131X
pISSN - 0277-6693
DOI - 10.1002/for.3980030302
Subject(s) - autoregressive integrated moving average , univariate , box–jenkins , computer science , econometrics , bayesian probability , kalman filter , series (stratigraphy) , range (aeronautics) , exponential smoothing , seasonal adjustment , hodrick–prescott filter , cover (algebra) , moving average , time series , artificial intelligence , machine learning , mathematics , variable (mathematics) , economics , multivariate statistics , materials science , business cycle , keynesian economics , engineering , composite material , biology , paleontology , mechanical engineering , mathematical analysis , computer vision
A large number of statistical forecasting procedures for univariate time series have been proposed in the literature. These range from simple methods, such as the exponentially weighted moving average, to more complex procedures such as Box–Jenkins ARIMA modelling and Harrison–Stevens Bayesian forecasting. This paper sets out to show the relationship between these various procedures by adopting a framework in which a time series model is viewed in terms of trend, seasonal and irregular components. The framework is then extended to cover models with explanatory variables. From the technical point of view the Kalman filter plays an important role in allowing an integrated treatment of these topics.