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<< /S /GoTo /D (section.1) >>
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(1 Introduction)
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<< /S /GoTo /D (section.2) >>
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(2 Exponential smoothing models for seasonal data)
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<< /S /GoTo /D (subsection.2.1) >>
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(2.1 Traditional approaches)
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<< /S /GoTo /D (subsection.2.2) >>
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(2.2 Modified models)
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<< /S /GoTo /D (subsection.2.3) >>
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(2.3 Trigonometric seasonal models)
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<< /S /GoTo /D (subsection.2.4) >>
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(2.4 Innovations state space formulations)
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<< /S /GoTo /D (subsubsection.2.4.1) >>
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(2.4.1 TBATS model)
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<< /S /GoTo /D (subsubsection.2.4.2) >>
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(2.4.2 BATS model)
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<< /S /GoTo /D (subsubsection.2.4.3) >>
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(2.4.3 Reduced forms)
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<< /S /GoTo /D (section.3) >>
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(3 Estimation, prediction, and model selection)
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<< /S /GoTo /D (subsection.3.1) >>
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(3.1 Estimation)
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<< /S /GoTo /D (subsection.3.2) >>
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(3.2 Prediction)
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<< /S /GoTo /D (subsection.3.3) >>
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(3.3 Model selection)
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<< /S /GoTo /D (subsubsection.3.3.1) >>
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(3.3.1 The use of an information criterion)
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<< /S /GoTo /D (subsubsection.3.3.2) >>
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(3.3.2 Selecting the number of harmonics ki in the trigonometric models)
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(3.3.3 Selecting the ARMA orders p and q for the models)
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<< /S /GoTo /D (section.4) >>
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(4 Applications of the proposed models)
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<< /S /GoTo /D (subsection.4.1) >>
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(4.1 Application to weekly US gasoline data)
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<< /S /GoTo /D (subsection.4.2) >>
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(4.2 Application to call center data)
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<< /S /GoTo /D (subsection.4.3) >>
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(4.3 Application to the Turkey electricity demand data)
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<< /S /GoTo /D (section.5) >>
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(5 Concluding remarks)
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