Have you considered adding the (sine, cosine) transformation of the time of day variable? This will ensure that the 0 and 23 hour for example are close to each other, thus allowing the cyclical nature of the variable to shine through.

Props to this blog-post for the idea:

https://medium.com/towards-data-science/top-6-errors-novice-machine-learning-engineers-make-e82273d394db

Have you considered adding the (sine, cosine) transformation of the time of day variable? This will ensure that the 0 and 23 hour for example are close to each other, thus allowing the cyclical nature of the variable to shine through.

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