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InceptionTime vs. Wavelet -- A comparison for time series classification

2024-03-27 · Daniel Klenkert, Daniel Schaeffer, Julian Stauch

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 %.

📄 PDF Abstract BibTeX arXiv:2403.18687

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ClassificationTime SeriesTime Series Classification

Methods 이 논문이 사용한 방법론

Average Pooling 설명 없음
Convolution A convolution is a type of matrix operation, consisting of a kernel, a small matrix of weights, that slides over input data performing element-wise multiplication with the…
Max Pooling Max Pooling is a pooling operation that calculates the maximum value for patches of a feature map, and uses it to create a downsampled (pooled) feature map. It is usually…
Kaiming Initialization 설명 없음
Global Average Pooling Global Average Pooling is a pooling operation designed to replace fully connected layers in classical CNNs. The idea is to generate one feature map for each corresponding…
InceptionTime 설명 없음

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