  With the increase of available time series data, predicting their class
labels has been one of the most important challenges in a wide range of
disciplines. Recent studies on time series classification show that
convolutional neural networks (CNN) achieved the state-of-the-art performance
as a single classifier. In this work, pointing out that the global pooling
layer that is usually adopted by existing CNN classifiers discards the temporal
information of high-level features, we present a dynamic temporal pooling (DTP)
technique that reduces the temporal size of hidden representations by
aggregating the features at the segment-level. For the partition of a whole
series into multiple segments, we utilize dynamic time warping (DTW) to align
each time point in a temporal order with the prototypical features of the
segments, which can be optimized simultaneously with the network parameters of
CNN classifiers. The DTP layer combined with a fully-connected layer helps to
extract further discriminative features considering their temporal position
within an input time series. Extensive experiments on both univariate and
multivariate time series datasets show that our proposed pooling significantly
improves the classification performance.
