Structure of NN for input data with drop out In financial markets, there is a simple problem of trading calendars varying across different countries. For example, Sweden observes Sweden National Day and Norway has Whit Monday. Typically, what happens then is that a time-series in the equity market that was closed for a holiday 'catches up' the next day when the market is open again.
For example:
+-----------------+------------+------------+------------+------------+
| Date            | SEK 1      | SEK 2      | NOK 1      | NOK 2      |
+-----------------+------------+------------+------------+------------+
| Date 1          | + 0.2%     | +0.4%      | +0.3%      | +0.6%      |
| Date 2          | + 1.1%     | +0.7%      | +0.3%      | +2.1%      |
| Date 3          | -3.2%      | -2.9%      | NaN        | NaN        |
| Date 4          | +0.1%      | -0.2%      | -2.8%      | -1.6%      |
+-----------------+------------+------------+------------+------------+

The objective of my model is to adjust the NOK returns for dates 3 and 4 since they have been distorted by the NOK holiday on Date 3. To do this I will use as many good dates as I have, such as date 1 and build a large dataset by randomly dropping out some data, adjusting the following the date and using the dropped out adjustments as the input and obviously I know the supervised output as the real data.
I feel that this would be well suited for a neural network, but I have never constructed one before with dropped out input.
Would it be appropriate to just build network where input data-points that are missing have no impact on the weights for that round? Are there any neural networks (or all) that do this by default?
There is no Special reason that neural networks are suitable, if anything it could be too much (depending on the complexities of your time-series).
In any case, you need to deal with you Nan values and if you are looking for a fast model, that imputes that automatically, I would suggest lgbm. Since it will impute NAN with values that reduce the out of fold loss the most (making the highly optimal imputation).