Forecasting daily time series using periodic unobserved components time series models
نویسندگان
چکیده
منابع مشابه
Forecasting daily time series using periodic unobserved components time series models
We explore a periodic analysis in the context of unobserved components time series models that decompose time series into components of interest such as trend, seasonal and irregular. Periodic time series models allow dynamic characteristics such as autocovariances to depend on the period of the year, month, week or day. In the standard multivariate approach one can interpret periodic time seri...
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Structural time series models are formulated in terms of components, such as trends, seasonals and cycles, that have a direct interpretation. As well as providing a framework for time series decomposition by signal extraction, they can be used for forecasting and for ‘nowcasting’ . The structural interpretation allows extensions to classes of models that are able to deal with various issues in ...
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This chapter is concerned with forecasting univariate seasonal time series data using periodic autoregressive models We show how one should account for unit roots and deterministic terms when generating out of sample forecasts We illus trate the models for various quarterly UK consumption series This is the rst version July of a chapter that is to be prepared for potential inclusion in the Comp...
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Periodic time series analysis refers to the modelling approach where important time series properties depend on the period of the year. The standard approach to time series modelling is to treat a time series as a stochastic process with seasonal fluctuations. In a periodic analysis seasonal variations are modelled using separate yearly time series for each season, which do not possess seasonal...
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ژورنال
عنوان ژورنال: Computational Statistics & Data Analysis
سال: 2006
ISSN: 0167-9473
DOI: 10.1016/j.csda.2005.09.009