A large margin time series nearest neighbour classification under locally weighted time warps

摘要

Accuracy of the k-nearest neighbour (\(k\hbox {NN}\)) classifier depends strongly on the ability of the used distance to induce k-nearest neighbours of the same class while keeping distant samples of different classes. For time series classification, \(k\hbox {NN}\) based on dynamic time warping (dtw) measure remains among the most popular and competitive approaches. However, by assuming time series uniformly distributed, standard dtw may show some limitations to classify complex time series. In this paper, we show how to enhance the potential of \(k\hbox {NN}\) under time warp measure by learning a locally weighted dynamic time warping. For that, first discriminative features are learned from the neighbourhoods, then used to weight time series elements to bring closer the k-nearest neighbours of the same class and move away the k-nearest neighbours of different classes. To evaluate the proposed method, a deep analysis and experimentation are conducted on 87 public datasets from different application domains, varying sizes and difficulty levels. The results obtained show significant improvement in the proposed weighted dtw for time series \(k\hbox {NN}\) classification.