Machine Learning
Pattern Extraction for Time Series Classification
PKDD '01 Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery
Interval and dynamic time warping-based decision trees
Proceedings of the 2004 ACM symposium on Applied computing
Fast time series classification using numerosity reduction
ICML '06 Proceedings of the 23rd international conference on Machine learning
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Boosting interval based literals
Intelligent Data Analysis
Supervised classification of share price trends
Information Sciences: an International Journal
Constructing High Dimensional Feature Space for Time Series Classification
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Time series shapelets: a new primitive for data mining
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Early prediction on time series: a nearest neighbor approach
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Support vector machines of interval-based features for time series classification
Knowledge-Based Systems
Dynamic time warping constraint learning for large margin nearest neighbor classification
Information Sciences: an International Journal
Weighted dynamic time warping for time series classification
Pattern Recognition
A shapelet transform for time series classification
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Automatic field data analyzer for closed-loop vehicle design
Information Sciences: an International Journal
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A tree-ensemble method, referred to as time series forest (TSF), is proposed for time series classification. TSF employs a combination of entropy gain and a distance measure, referred to as the Entrance (entropy and distance) gain, for evaluating the splits. Experimental studies show that the Entrance gain improves the accuracy of TSF. TSF randomly samples features at each tree node and has computational complexity linear in the length of time series, and can be built using parallel computing techniques. The temporal importance curve is proposed to capture the temporal characteristics useful for classification. Experimental studies show that TSF using simple features such as mean, standard deviation and slope is computationally efficient and outperforms strong competitors such as one-nearest-neighbor classifiers with dynamic time warping.