27 lines
1.4 KiB
TeX
27 lines
1.4 KiB
TeX
\subsection{Effects of the Pixel Size and Time Step Length}
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\label{pixels_intervals}
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As elaborated in Sub-section \ref{grid}, more order aggregation leads to a
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higher overall demand level and an improved pattern recognition in the
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generated time series.
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Consequently, individual cases tend to move to the right in tables equivalent
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to Table \ref{t:results}.
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With the same $ADD$ clusters, forecasts for pixel sizes of $2~\text{km}^2$ and
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$4~\text{km}^2$ or time intervals of 90 and 120 minutes or combinations
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thereof yield results similar to the best models as revealed in Tables
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\ref{t:results}, \ref{t:hori}, \ref{t:vert}, and \ref{t:ml} for high
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demand.
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By contrast, forecasts for $0.5~\text{km}^2$ pixels have most of the cases
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(i.e., $n$) in the no or low demand clusters.
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In that case, the pixels are too small, and pattern recognition becomes
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harder.
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While it is true, that \textit{trivial} exhibits the overall lowest MASE
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for no demand cases, these forecasts become effectively worthless for
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operations.
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In the extreme, with even smaller pixels we would be forecasting $0$ orders
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in all pixels for all time steps.
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In summary, the best model and its accuracy are determined primarily by the
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$ADD$, and the pixel size and interval length are merely parameters to
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control that.
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The forecaster's goal is to create a grid with small enough pixels without
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losing a recognizable pattern.
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