I have a dataset where I have 12000+ data points and 25 features out of which last feature is the class label. This is classification problem. Now, I want to convert every data points into image, . I have no idea how to do that. Please help. I work on Python. If anyone have could provide sample code I will be grateful. Thanks in advance.
There is already some work on that, you can use either Gramian Angular Fields (GAF) or Markov Transition Fields (MTF), a good description is in Imaging Time-Series to Improve Classification and Imputation. Also, some other works used recurrent plots as Deep-Gap: deep learning framework. Imaging TS is an interesting way to think about them so you can use e.g. CNNs easily. But which method you like to use? BTW be aware this might not be an "efficient" way to classify time series :)