Scaling up dynamic time warping for datamining applications
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Ensembling neural networks: many could be better than all
Artificial Intelligence
Efficient Time Series Matching by Wavelets
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
On the Need for Time Series Data Mining Benchmarks: A Survey and Empirical Demonstration
Data Mining and Knowledge Discovery
Large Scale Multiple Kernel Learning
The Journal of Machine Learning Research
First International Workshop and Challenge on Time Series Classification
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Automatic record linkage using seeded nearest neighbour and support vector machine classification
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the VLDB Endowment
Stacked generalization: when does it work?
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Time-Series Classification Based on Individualised Error Prediction
CSE '10 Proceedings of the 2010 13th IEEE International Conference on Computational Science and Engineering
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Time series classification, due to its applications in various domains, is one of the most important data-driven decision tasks of artificial intelligence. Recent results show that the simple nearest neighbor method with an appropriate distance measure performs surprisingly well, outperforming many state-of-the art methods. This suggests that the choice of distance measure is crucial for time series classification. In this paper we shortly review the most important distance measures of the literature, and, as major contribution, we propose a framework that allows fusion of these different similarity measures in a principled way. Within this framework, we develop a hybrid similarity measure. We evaluate it in context of time series classification on a large, publicly available collection of 35 real-world datasets and we show that our method achieves significant improvements in terms of classification accuracy.