Machine Learning
Interval and dynamic time warping-based decision trees
Proceedings of the 2004 ACM symposium on Applied computing
Supervised Clustering " Algorithms and Benefits
ICTAI '04 Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence
Temporal classification: extending the classification paradigm to multivariate time series
Temporal classification: extending the classification paradigm to multivariate time series
Proceedings of the VLDB Endowment
Evolving and clustering fuzzy decision tree for financial time series data forecasting
Expert Systems with Applications: An International Journal
Analysis of time series data with predictive clustering trees
KDID'06 Proceedings of the 5th international conference on Knowledge discovery in inductive databases
Trend discovery in financial time series data using a case based fuzzy decision tree
Expert Systems with Applications: An International Journal
Time series shapelets: a novel technique that allows accurate, interpretable and fast classification
Data Mining and Knowledge Discovery
Segment and combine approach for non-parametric time-series classification
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Experimental evaluation of time-series decision tree
AM'03 Proceedings of the Second international conference on Active Mining
Analysis of time series of graphs: prediction of node presence by means of decision tree learning
MLDM'05 Proceedings of the 4th international conference on Machine Learning and Data Mining in Pattern Recognition
Fuzzy decision trees: issues and methods
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Nowadays with time series accounting for an increasingly large fraction of world's supply of data, there has been an explosion of interest in mining time series data. This paper proposes an approach of creating a new data structure automatically, for multivariate time series classification. For more accurate and comprehensive classification, induction of valuable rules named soft discretisation decision forest is illustrated comparing with other machine learning methods such as traditional neural network, SVM and nearest neighbour algorithms. Moreover, some real time series instances from the training dataset will be selected as class dedicated patterns. And a splitting stage using fuzzy theory is prepared for comparing attributes of time series. The ideas of authors are confirmed by simulation results with a set of Japanese vowel time series capably.