Abstract: Ensemble learning is an effective method to classify multivariate time series. However, ensemble learning requires higher performance of the base classifier, and the combination of base classifier algorithms has a greater impact on the classification effect. This paper proposes a multivariate D-S(Dempster/ Shafer) evidence weighted ensemble classification method based on shapelets. First, learning the base classifier Shapelets on the univariate time series, the classification accuracy of the base classifier is determined as its weight in the multi-classifier. Shapelets are sub-sequences of time series. There is no dependency between different variable Shapelets, and the classification accuracy of a single Shapelets is high, and a “good but different” base classifier can be obtained. Then, a weighted probability assignment algorithm is proposed to increase the weight of the base classifier with high classification accuracy and reduce the weight of the base classifier with low classification accuracy. Two combination strategies are added to eliminate evidence conflicts and improve efficiency. Compared to some state-of-the-art algorithms on the standard dataset, our algorithm can obtain a better classification result.

Key words: shapelets, multivariate time series, ensemble learning, dempster/ shafer(D-S) evidence theory