InceptionTime vs. Wavelet -- A comparison for time series classification
This work addresses time series classification for infrasound data, but it is incremental as it compares existing methods without introducing new techniques.
The paper compared two neural network approaches for classifying infrasound time series data: a direct method using InceptionTime achieved 95.2% accuracy, and a wavelet-based method using ResNet achieved over 90% accuracy.
Neural networks were used to classify infrasound data. Two different approaches were compared. One based on the direct classification of time series data, using a custom implementation of the InceptionTime network. For the other approach, we generated 2D images of the wavelet transformation of the signals, which were subsequently classified using a ResNet implementation. Choosing appropriate hyperparameter settings, both achieve a classification accuracy of above 90 %, with the direct approach reaching 95.2 %.