29 lines
1.4 KiB
TeX
29 lines
1.4 KiB
TeX
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\subsection{Overall Approach}
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\label{approach_approach}
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On a conceptual level, there are three distinct aspects of the model
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development process.
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First, a pre-processing step transforms the platform's tabular order data into
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either time series in Sub-section \ref{grid} or feature matrices in
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Sub-section \ref{ml_models}.
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Second, a benchmark methodology is developed in Sub-section \ref{unified_cv}
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that compares all models on the same scale, in particular, classical
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models with ML ones.
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Concretely, the CV approach is adapted to the peculiar requirements of
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sub-daily and ordinal time series data.
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This is done to maximize the predictive power of all models into the future
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and to compare them on the same scale.
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Third, the forecasting models are described with respect to their assumptions
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and training requirements.
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Four classification dimensions are introduced:
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\begin{enumerate}
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\item \textbf{Timeliness of the Information}:
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whole-day-ahead vs. real-time forecasts
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\item \textbf{Time Series Decomposition}: raw vs. decomposed
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\item \textbf{Algorithm Type}: "classical" statistics vs. ML
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\item \textbf{Data Sources}: pure vs. enhanced (i.e., with external data)
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\end{enumerate}
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Not all of the possible eight combinations are implemented; instead, the
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models are varied along these dimensions to show different effects and
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answer the research questions.
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