Early Classification of Time Series. Cost-based Optimization Criterion and Algorithms
This work addresses the need for timely predictions in applications like healthcare or finance, though it is incremental as it builds on existing early classification methods.
The paper tackles the problem of early classification of time series by balancing accuracy and decision delay, introducing a new cost-based optimization criterion and non-myopic algorithms that outperform a competitive state-of-the-art method in experiments on real datasets.
An increasing number of applications require to recognize the class of an incoming time series as quickly as possible without unduly compromising the accuracy of the prediction. In this paper, we put forward a new optimization criterion which takes into account both the cost of misclassification and the cost of delaying the decision. Based on this optimization criterion, we derived a family of non-myopic algorithms which try to anticipate the expected future gain in information in balance with the cost of waiting. In one class of algorithms, unsupervised-based, the expectations use the clustering of time series, while in a second class, supervised-based, time series are grouped according to the confidence level of the classifier used to label them. Extensive experiments carried out on real data sets using a large range of delay cost functions show that the presented algorithms are able to satisfactorily solving the earliness vs. accuracy trade-off, with the supervised-based approaches faring better than the unsupervised-based ones. In addition, all these methods perform better in a wide variety of conditions than a state of the art method based on a myopic strategy which is recognized as very competitive.