  One of the challenging questions in time series forecasting is how to find
the best algorithm. In recent years, a recommender system scheme has been
developed for time series analysis using a meta-learning approach. This system
selects the best forecasting method with consideration of the time series
characteristics. In this paper, we propose a novel approach to focusing on some
of the unanswered questions resulting from the use of meta-learning in time
series forecasting. Therefore, three main gaps in previous works are addressed
including, analyzing various subsets of top forecasters as inputs for
meta-learners; evaluating the effect of forecasting error measures; and
assessing the role of the dimensionality of the feature space on the
forecasting errors of meta-learners. All of these objectives are achieved with
the help of a diverse state-of-the-art pool of forecasters and meta-learners.
For this purpose, first, a pool of forecasting algorithms is implemented on the
NN5 competition dataset and ranked based on the two error measures. Then, six
machine-learning classifiers known as meta-learners, are trained on the
extracted features of the time series in order to assign the most suitable
forecasting method for the various subsets of the pool of forecasters.
Furthermore, two-dimensionality reduction methods are implemented in order to
investigate the role of feature space dimension on the performance of
meta-learners. In general, it was found that meta-learners were able to defeat
all of the individual benchmark forecasters; this performance was improved even
after applying the feature selection method.
