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Obvious answer is rounding up/down but is the best?

That is, continue to "center" the histograms!

Some hints:

• Use Mean Absolute Error (MAE): Mean Square Error (MSE) basically tends to estimate your average, while MAE tends to estimate the median, this can help us. Additionally, it is theorically possible to get even more savvy for heavy tailed distributions (think distributions with big outliers), some background at: http://www.mitpressjournals.org/doi/pdf/10.1162/08997660260293300
• Use Concrete Dropout, as it turns out, part of the issue is that the dropout probabilities are parameters themselves (remember the hyperparameter and grid search thing we talked about?). However, our friend Yarin Gal who came up with the uncertainty things, also came up with a way to estimate the desired dropout probabilities, the method is called ConcreteDropour. You can read more here https://arxiv.org/abs/1705.07832, check the code at the very end, it is in Keras!

Theory here: http://mlg.eng.cam.ac.uk/yarin/blog_2248.html

It is possible to estimate the uncertainty of a DL model using dropout in the training and in the sampling. Basically, the idea is that in order to estimate the uncertainty of an LSTM network, it is simply necessary to sample T times the network with dropout (that is on each sample, we drop randomly some of the nodes).

This task is basically to extend the current approach to one that includes uncertainty. Specifically, the multistep should return the mean multistep prediction and the standard deviation multistep of the samples.

Relevant links:

• http://mlg.eng.cam.ac.uk/yarin/blog_3d801aa532c1ce.html
• http://machinelearningmastery.com/sequence-classification-lstm-recurrent-neural-networks-python-keras/
• https://keras.io/layers/recurrent/#simplernn

Where Can I get this file 'paxout_table.csv ' ?

a) treat each station as an independent TS
b) treat the problem as a single multi-dimensional TS
c) BONUS: b) but instead of 66 stations, compute all 66x66 origin/destination demand pairs.