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Alexander Hess 2020-10-09 14:48:10 +02:00
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@ -10,8 +10,9 @@ Somewhat surprisingly, despite ML-based methods` popularity in both business
and academia in recent years, we must conclude that classical forecasting and academia in recent years, we must conclude that classical forecasting
methods suffice to reach the best accuracy in our study. methods suffice to reach the best accuracy in our study.
There is one case where ML-based methods are competitive in our case study: There is one case where ML-based methods are competitive in our case study:
In a high demand pixel, if only about four to six weeks of past data is In a high demand pixel (defined as more than 25 orders per day on average),
available, the \textit{vrfr} model outperformed the classical ones. if only about four to six weeks of past data is available,
the \textit{vrfr} model outperformed the classical ones.
So, we recommend trying out ML-based methods in such scenarios. So, we recommend trying out ML-based methods in such scenarios.
In addition, with the \textit{hsma} and \textit{hets} models being the overall In addition, with the \textit{hsma} and \textit{hets} models being the overall
winners, incorporating real-time data is not beneficial, in particular, winners, incorporating real-time data is not beneficial, in particular,
@ -53,6 +54,8 @@ We emphasize that for the most part, our proposed forecasting system
is calibrated automatically and no manual work by a data scientist is required. is calibrated automatically and no manual work by a data scientist is required.
The only two parameters where assumptions need to be made are the pixel size The only two parameters where assumptions need to be made are the pixel size
and the time step. and the time step.
While they can only be optimized by the data scientist over time, the results in our The results in our empirical study suggest
empirical study suggest that a pixel size of $1~\text{km}^2$ and a time step of that a pixel size of $1~\text{km}^2$ and a time step of one hour are ideal,
one hour are ideal. which results in the optimal trade-off
between signal strength and spatial-temporal resolution.
Future research may explore adaptive grid-sizing depending on, for instance, demand density.