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Time Series Classification with a convolutional neural network ?

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Hello Community,
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!

Accepted Answer

Aditya Patil
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.

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