  Time series motifs play an important role in the time series analysis. The
motif-based time series clustering is used for the discovery of higher-order
patterns or structures in time series data. Inspired by the convolutional
neural network (CNN) classifier based on the image representations of time
series, motif difference field (MDF) is proposed. Compared to other image
representations of time series, MDF is simple and easy to construct. With the
Fully Convolution Network (FCN) as the classifier, MDF demonstrates the
superior performance on the UCR time series dataset in benchmark with other
time series classification methods. It is interesting to find that the triadic
time series motifs give the best result in the test. Due to the motif
clustering reflected in MDF, the significant motifs are detected with the help
of the Gradient-weighted Class Activation Mapping (Grad-CAM). The areas in MDF
with high weight in Grad-CAM have a high contribution from the significant
motifs with the desired ordinal patterns associated with the signature patterns
in time series. However, the signature patterns cannot be identified with the
neural network classifiers directly based on the time series.
