14 lines
767 B
TeX
14 lines
767 B
TeX
\subsection{Demand Forecasting with Machine Learning Methods}
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\label{ml_methods}
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ML methods have been employed in all kinds of prediction tasks in recent
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years.
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In this section, we restrict ourselves to the models that performed well in
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our study: Random Forest (RF) and Support Vector Regression (SVR).
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RFs are in general well-suited for datasets without a priori knowledge about
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the patterns, while SVR is known to perform well on time series data, as
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shown by \cite{hansen2006} in general and \cite{bao2004} specifically for
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intermittent demand.
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Gradient Boosting, another popular ML method, was consistently outperformed by
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RFs, and artificial neural networks require an amount of data
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exceeding what our industry partner has by far.
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