Journal of Forecasting · 1984 · 246 citations · 57 references
ComputingMathematicsBayesian Data AnalysisForecastingTime Series AnalysisForecasting MethodsKalman FilteringTime Series EconometricsTime Series ForecastingTime Series ModelingTime Series PredictionArima ModelsBayesian ForecastingBox-jenkins MethodologyStatistical Time Series ModelsUnivariate AnalysisUnivariate Forecasting
A large number of statistical forecasting procedures for univariate time series have been proposed, ranging from simple methods such as exponentially weighted moving averages to complex approaches like Box–Jenkins ARIMA and Harrison–Stevens Bayesian forecasting. The paper aims to demonstrate the relationships among these procedures by framing time series models in terms of trend, seasonal, and irregular components. The framework is extended to include models with explanatory variables and employs the Kalman filter to integrate these topics.
Abstract 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.
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Time Series Analysis: Forecasting and Control
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Computing, Data Science, Forecasting +11
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