Hi, I am doing a project which requires to directly input the time-series sensor data, such as acceleration and angular velocity, to the regression-based deep learning model for predicting a score of movement for each subject.
However, I noticed that there are quite few subjects with a certain range of score, and the accuracy of the model dropped when the score of the subject for testing is in this range.
I have read the documentation of SMOTE and it seems that SMOTE-based algorithm are mainly used for augmenting the features, not time-series raw data.
Is that possible to directly apply the SMOTE-based algorithm to the time-series raw data?
Thank you so much in advance!
Hi, I am doing a project which requires to directly input the time-series sensor data, such as acceleration and angular velocity, to the regression-based deep learning model for predicting a score of movement for each subject. However, I noticed that there are quite few subjects with a certain range of score, and the accuracy of the model dropped when the score of the subject for testing is in this range. I have read the documentation of SMOTE and it seems that SMOTE-based algorithm are mainly used for augmenting the features, not time-series raw data. Is that possible to directly apply the SMOTE-based algorithm to the time-series raw data? Thank you so much in advance!