# Training LSTM for time series prediction with nan labels

I have a time series of features $$x_1,x_2,x_3,...,x_n$$. I want to make a prediction $$y_1,y_2,y_3,...,y_n$$ for each timestep. However, in my training data some of the $$y$$ can be nan. I'd like the fit to just ignore these (i.e. the cost for this pair measured $$y$$ and predicted $$y$$ is zero). I'm currently using tensorflow through Keras.

Is there an analogue of the masking layer for the label? I'm currently using tensorflow through Keras. Alternatively, it might be possible to change the loss function, but I don't know how, expecially while retaining numerical efficiency.

You can use mean fill or predict that missing y value. Given the $$y_1,y_2,y_3,\cdots ,y_n$$ for each training sample, you can fill in the missing by mean of that $$y$$ across training examples, or interpolate