Time Series Classification with a convolutional neural network ?
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my lack of experience in machine learning leads me to ask you guys. I want to classify different time series.
A bit background:
I measured the movement of my skateboard while doing tricks with an IMU ( gyroscope, accelerometer, magnetometer). I did 4 different tricks, each 50 times. Than i cute the long time series with all tricks in it into samples ( 1 sample is 1 trick each with the same lenght) and sorted by trick (class).
My goal would be, that I can show my machine data and it can classify by its class ( type of trick).
I read alot about this things and I think there are many ways to acchive this. I read that a convolutional neural network or a decision tree could be a good solution. What do you think - any suggestions ?
Would appreaciate all answers.
Have a good day!
Aditya Patil on 14 Jul 2021
As the data is temporal, you can use one of the sequence classification models. For example, you can use LSTMs (Long Short-Term Memory Networks). See the sequence classification using Deep Learning example https://www.mathworks.com/help/deeplearning/ug/classify-sequence-data-using-lstm-networks.html.
Alternately, if you know that the data can be represented well in structural format, you can use any of the classification algorithms/models available in Statistics and Machine Learning Toolbox, or in Deep Learning Toolbox.
I also recommend looking for pretrained models for this task and trying transfer learning.