So in the following Thread it is discussed about augmentation for time series: Data Augmentation strategies for Time Series Forecasting
The first answer refers among others to the following:
Window Slicing (WS) A first method that is inspired from the computer vision community [8,10] consists in extracting slices from time series and performing classification at the slice level. This method has been introduced for time series in [6]. At training, each slice extracted from a time series of class y is assigned the same class and a classifier is learned using the slices. The size of the slice is a parameter of this method. At test time, each slice from a test time series is classified using the learned classifier and a majority vote is performed to decide a predicted label. This method is referred to as window slicing (WS) in the following.
So I have time series and I want to do window slicing in order to train a CNN with the different slices. So is my understanding for slicing correct as my following image shows. Is option a) or b) correct or neither nor?
