Don't think it is possible as predicting something always depends more on what is just before, so can not go far in time
Instead of predicting one step at a time, can we train models that predict only future events they are confident about? Predict the predictable moments in an unpredictable system. These are useful for control. Time agnostic prediction (D. Jayaraman et al): https://arxiv.org/abs/1808.07784 pic.twitter.com/oLEsZPTLE4
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Anything with periodicity or seasonality can be predicted somewhat far in advance. My initial guess at the task used here was a function with periodicity in the noise.
End of conversation
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