CNN Approaches for Time Series Classification DOI: http://dx.doi.org/10.5772/intechopen.81170

capture most of entire peaks present in the input signals. By obtaining the most appropriate 1st convolutional layer filter, there will be no need to apply multiple branches with different 1st convolutional layer filter sizes, and no need to apply transformations such as down-sampling, slicing and warping, thus requiring less computational resources. The question of how to compute this adaptive 1st convolutional layer filter is addressed in [4]. In this section, we will discuss the approach based on the adaptive 1st convolutional layer filter. Next, to prove the efficiency of this/our approach, an application on SMM recognition is conducted and results are analyzed.
