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I am trying to classify raw accelerometer data x,y,z to its corresponding label.

What is the best architecture for best results?

Or, does anyone have any suggestions on LSTM architectures built on keras with input and output nodes?

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I would not use the word "best" but LSTM-RNN are very powerful when dealing with timeseries, simply because they can store information about previous values and exploit the time dependencies between the samples. That said, it is definitely worth going for it.

It has been proven that their performance can be boosted significantly if they are combined with a Convolutional Neural Network (CNN) that can learn the spatial structures in your data, which in this case is one-dimensional.

Take a look at this state-of-the-art method that combines LSTM and CNN, published very recently (this year).

Apart from that, take a look at this coding example, it explains how to use Keras (Python) to implement a LSTM network for sequence classification and how to combine it with a CNN for augmented performance.

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