Hi team :), Good work!!!
You are right that the issue with missings should be solved in a better way (but not replacing with last known value). If there is one or a few neighbour missings, we may replace them without distorting the data, but in the case of long missing interval, Instead of replacement, we may use the data sets separately… There are ways to concatenate data from different data sets even when build dynamic models…

Not more to add after Agamemnon 🙂
I also like your validation approach, keeping in mind the small number of data, also introducing different scenarios related to the forecasts.

You have made interesting analyses… Can you tell me a bit more about the usage of market entropy for each period?

🙂 Yes, OK, Thanks for the long answer. It is always helpful to have domain expertise.

Hi team :), Good work!!!
I like your idea to use NN. How you selected the NN structure? If you have described this step – sory, I have missed it.
May about missings there are better ways to handle, espacially when the missing period is long…