I suppose Python 3.6 and tensorflow 1.13 should work fine with the code.

• Python 3.7
• Tensorflow 1.14.1 (tf-nightly)
• Keras 2.2.4

• install any necessary packages (requirements.txt is not optimised as it inherits my base conda environment)
• start notebooks/5-model-evaluation and look for Evaluation/Submission tab

I tried to be minimalistic and realistic. While I was given the choice to pick up to 2 weeks of historical data, I found that with only 8 weeks of data, if I did a walk-forward split of my training data, I would end up with about 4 weeks of training data (with very little validation data).

• normalized distance from hand-picked POI (qp09d8, qp03xx, qp03wf)
• datetime features day, hour, per-fifteen-mins - sin/cos equivalent
• last 2 hours demands - T, T-1, T-2, .. T-7
• last 7 days demands of the predicted time window +/- 1 hour - (day-1 T-3, T-2 ... T+9), (day-2 T-3 ... T+9) ... (day-7 T-3, ... T+9)

• Last 7 days features feed into a RNN
• Last 7 days features feed into a CNN
• Last 2 hours features feed into a RNN
• Distance/datetime features feed into a DNN
• Last 7th day features feed into a DNN (act as attention)
• Outputs of all above serve as metadata and feed into another DNN to predict next 5 demands

This is rather tricky and time-consuming. I tried a rather complex model with more nodes and layers, it turned out the complex model converges to bad local minima very easily so I decided to give that up after an overnight hyperparameter tuning job and still couldn't yield any satisfying result. While relying on a lucky seed and learning rate, I was able to hit a relatively decent result using a less complex model.

• https://github.com/Arturus/kaggle-web-traffic
• https://gist.github.com/thousandvoices/7d01f366a388516359915a4b090e29d4
• https://github.com/jfpuget/Kaggle/tree/master/WebTrafficPrediction
• https://blog.davidkaleko.com/feature-engineering-cyclical-features.html