Frequency domain characteristics of linear operator to decompose a time series into the multi-components |
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Authors: | T Higuchi |
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Institution: | (1) The institute of Statistical Mathematics, 4-6-7 Minami-Azabu, Minato-ku, 106 Tokyo, Japan |
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Abstract: | Frequency domain properties of the operators to decompose a time series into the multi-components along the Akaike's Bayesian model (Akaike (1980, Bayesian Statistics, 143–165, University Press, Valencia, Spain)) are shown. In that analysis a normal disturbance-linear-stochastic regression prior model is applied to the time series. A prior distribution, characterized by a small number of hyperparameters, is specified for model parameters. The posterior distribution is a linear function (filter) of observations. Here we use frequency domain analysis or filter characteristics of several prior models parametrically as a function of the hyperparameters. |
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Keywords: | Time series Bayesian approach signal decomposition linear filter variable kernel curve smoothing smoothness prior seasonal component model quasi-sinusoidal wave extraction |
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