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On the Benefit of Using Time Series Features for Choosing a Forecasting Method
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Abstract
In research of time series forecasting, a lot of uncertainty is
still related to the question of which forecasting method to use in which
situation. One thing is obvious: There is no single method that performs
best on all time series. This work examines whether features extracted
from time series can be exploited for a better understanding of different
behaviour of forecasting algorithms. An extensive pool of automatically
computable features is identified, which is submitted to feature selection
algorithms. Finally, a possible relationship between these features and
the performance of forecasting and forecast combination methods for the
particular series is investigated