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Remove glossary

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Alexander Hess 2020-11-30 18:42:54 +01:00
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@ -40,8 +40,7 @@ Their main advantages stem from the fact that the models calibrate themselves
\cite{cleveland1990} introduce a seasonal and trend decomposition using a
repeated locally weighted regression - the so-called Loess procedure - to
smoothen the trend and seasonal components, which can be viewed as a
generalization of the methods above and is denoted by the acronym
\gls{stl}.
generalization of the methods above and is denoted by the acronym STL.
In contrast to the X11, X13, and SEATS methods, the STL supports seasonalities
of any lag $k$ that must, however, be determined with additional
statistical tests or set with out-of-band knowledge by the forecaster