Artificial Intelligence Review - Special issue on lazy learning
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Advances in Large Margin Classifiers
Advances in Large Margin Classifiers
On the need for time series data mining benchmarks: a survey and empirical demonstration
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Transforming classifier scores into accurate multiclass probability estimates
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Indexing multi-dimensional time-series with support for multiple distance measures
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Improving the Reliability of Decision Tree and Naive Bayes Learners
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
The attribute selection problem in decision tree generation
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
A non-parametric learning algorithm for small manufacturing data sets
Expert Systems with Applications: An International Journal
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This study investigates the effectiveness of probability forecasts output by standard machine learning techniques (Neural Network, C4.5, K-Nearest Neighbours, Naive Bayes, SVM and HMM) when tested on time series datasets from various problem domains. Raw data was converted into a pattern classification problem using a sliding window approach, and the respective target prediction was set as some discretised future value in the time series sequence. Experiments were conducted in the online learning setting to model the way in which time series data is presented. The performance of each learner's probability forecasts was assessed using ROC curves, square loss, classification accuracy and Empirical Reliability Curves (ERC) [1]. Our results demonstrate that effective probability forecasts can be generated on time series data and we discuss the practical implications of this.