"Missing token" for time series data

Just as there are “Missing tokens” in language modeling problems to direct prediction to only the missing “words”, is there an equivalent “missing token” for numerical values to impute missing values in a time series? This feels trivial but I’m seemingly stumped on how to do this!

Hello, If I have understood you question correctly. You can treat your “numerical values in a time series” as series of characters or series of words in a language model.

@AbdulsalamBande I see, but I feel that is cumbersome. Say I want really high precision, a floating point number broken into “characters” would be really long.

Is there a standard workflow where the network’s attention can be directed to only missing numerical values? For instance, if my input is

input = np.array([[0.5, 1.2, 0.2], [0.6, 1.3, 0.3], [np.nan, np.nan, np.nan], [0.8, 1.4, 0.5], [0.9, 1.5, 0.6]])

I’d like the model to 1) copy over the input values where it is not missing 2) calculate the loss based on the prediction of only the missing values (np.nan).