Now I have a task to classify the imbalanced time series datasets using ML classifiers, such as Logistic Regression, Decision Tree, SVM, and KNN. I am not allowed to use the Deep Learning tools, such as CNN and RNN. The time series data is measurements of the Force-Displacement Curve from a production line. The dataset is extremely imbalanced (minority class: majority class= 1:100). So I want to use Data Augmentation techniques to enlarge the size of the minority class, in order to optimize the performance of the classifiers and avoid overfitting.
I have tried many tools in feature-space, such as oversampling, undersampling, SMOTE, ADASYN and so on. But their performance is not so perfect. And I wish to generate synthetic time series data using Data Augmentation techniques, based on the initial data. Similar to Image Recognition, I have also tried the exiting methods which have been applied to images, such as scaling, rotation, and jittering. But they are also not so useful.
So I want to ask if there are any other DA techniques to generate synthetic time series data. I have only some initial ideas, such as using DTW, Fournier Transform, Markov Chain and so on, but no papers or code about applying them.
Can anyone help me? I really appreciate your help. Thank you!