Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Combining and selecting forecasting models using rule based induction
Computers and Operations Research
Learning the Empirical Hardness of Optimization Problems: The Case of Combinatorial Auctions
CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Cross-disciplinary perspectives on meta-learning for algorithm selection
ACM Computing Surveys (CSUR)
Empirical hardness models: Methodology and a case study on combinatorial auctions
Journal of the ACM (JACM)
Particle Swarm Model Selection
The Journal of Machine Learning Research
SATzilla: portfolio-based algorithm selection for SAT
Journal of Artificial Intelligence Research
A portfolio approach to algorithm select
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Hierarchical hardness models for SAT
CP'07 Proceedings of the 13th international conference on Principles and practice of constraint programming
SATzilla-07: the design and analysis of an algorithm portfolio for SAT
CP'07 Proceedings of the 13th international conference on Principles and practice of constraint programming
A Field Guide to Genetic Programming
A Field Guide to Genetic Programming
Practical performance models of algorithms in evolutionary program induction and other domains
Artificial Intelligence
Performance prediction and automated tuning of randomized and parametric algorithms
CP'06 Proceedings of the 12th international conference on Principles and Practice of Constraint Programming
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One of the first steps when approaching any machine learning task is to select, among all the available procedures, which one is the most adequate to solve a particular problem; in automated problem solving this is known as the algorithm selection problem. Of course, this problem is also present in the field of time series forecasting, there, one needs to select the forecaster that makes the most accurate predictions. Generally, this selection task is manually performed by analyzing the characteristics of the time series, thus relying on the expertise that one has on the available forecasters. In this paper, we propose an automatic procedure to choose a forecaster given a set of candidates, i.e., to solve the algorithm selection problem on this domain. To do so, we follow two paths. Firstly, we propose to model the performance of the forecasters using a linear combination of features that were previously used to assess the problem difficulty of evolutionary algorithms, together with a set of features we propose in this paper. Then, this model is used to predict the performance of the forecasters and based on these predictions the forecaster is selected. Our second approach is to treat this algorithm selection process as a classification task where the descriptors of each time series are the proposed features. To show the capabilities of our approach, we test the forecasters on the time series of the M1 and M3 time series competitions and used three different forecasters. In all the cases tested, our proposals outperform the performance of the three forecasters indicating the viability of our approach.