Machine Learning
Combining and selecting forecasting models using rule based induction
Computers and Operations Research
Ensemble learning via negative correlation
Neural Networks
A perspective view and survey of meta-learning
Artificial Intelligence Review
Selection of Time Series Forecasting Models based on Performance Information
HIS '04 Proceedings of the Fourth International Conference on Hybrid Intelligent Systems
Managing Diversity in Regression Ensembles
The Journal of Machine Learning Research
The lack of a priori distinctions between learning algorithms
Neural Computation
Using machine learning techniques to combine forecasting methods
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
Editorial: European Symposium on Times Series Prediction
Neurocomputing
A novel weighted ensemble technique for time series forecasting
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
A fuzzy evolutionary framework for combining ensembles
Applied Soft Computing
Models of performance of time series forecasters
Neurocomputing
International Journal of Applied Evolutionary Computation
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In research of time series forecasting, a lot of uncertainty is still related to the task of selecting an appropriate forecasting method for a problem. It is not only the individual algorithms that are available in great quantities; combination approaches have been equally popular in the last decades. Alone the question of whether to choose the most promising individual method or a combination is not straightforward to answer. Usually, expert knowledge is needed to make an informed decision, however, in many cases this is not feasible due to lack of resources like time, money and manpower. This work identifies an extensive feature set describing both the time series and the pool of individual forecasting methods. The applicability of different meta-learning approaches are investigated, first to gain knowledge on which model works best in which situation, later to improve forecasting performance. Results show the superiority of a ranking-based combination of methods over simple model selection approaches.