31 lines
1.6 KiB
TeX
31 lines
1.6 KiB
TeX
\subsection{Impact of the Training Horizon}
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\label{training}
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Whereas it is reasonable to assume that forecasts become more accurate as the
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training horizon expands, our study reveals some interesting findings.
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First, without demand, \textit{trivial} indeed performs better with more
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training material, but improved pattern recognition cannot be the cause
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here.
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Instead, we argue that the reason for this is that the longer there has been
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no steady demand, the higher the chance that this will not change soon.
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Further, if we focus on shorter training horizons, the sample will necessarily
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contain cases where a pixel is initiated after a popular-to-be restaurant
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joined the platform:
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Demand grows fast making \textit{trivial} less accurate, and the pixel moves
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to another cluster soon.
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Second, with low demand, the best-performing \textit{hsma} becomes less
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accurate with more training material.
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While one could argue that this is due to \textit{hsma} not fitting a trend,
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the less accurate \textit{hses} and \textit{hets} do fit a trend.
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Instead, we argue that any low-demand time series naturally exhibits a high
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noise-to-signal ratio, and \textit{hsma} is the least susceptible to
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noise.
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Then, to counter the missing trend term, the training horizon must be shorter.
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With medium demand, a similar argument can be made; however, the
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signal already becomes more apparent favoring \textit{hets} with more
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training data.
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Lastly, with high demand, the signal becomes so clear that more sophisticated
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models can exploit longer training horizons.
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