#StackBounty: #machine-learning #time-series #neural-networks #forecasting Predicting walking routes using PyTorch

Bounty: 100

I’m working on a project that uses sensors to monitor a persons location. These devices simply record the current GPS coordinates and ping them back to a server (the coordinates will then be converted to a simple-grid representation to account for noise in GPS). What I want to try and do is after recording a users routes, train a neural network to predict the next coordinates, i.e. take the example below where a user repeats only two routes over time, Home->A and Home->B.

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I want to implement a model that once trained, could then be fed with real-time coordinates e.g. (15, 4), (14, 4), (13, 4) and predict that the next location is (12, 4), or with the other route (15, 10), (15, 11), (15, 12) and predict that (14, 12) is next.

I had success using LSTMs in Keras before, but that was essentially a regression problem e.g. yearly sales. I initially assumed an RNN/LSTM approach would be best for this route prediction, but now I’m not so sure since each route is discreet, how would I decided what part of the sequence is designated input and which as output for training.

Any ideas, guidance on how to tackle this problem would be really appreciated! I appreciate that there might be better non-neural net approaches, but I really want to try and use deep learning to tackle this. I’m currently working in PyTorch if that helps in terms of implementation options.

Note: I wasn’t sure if this was a better fit here or Stack Overflow, so please shout if it should be moved.

Get this bounty!!!

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