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EManO13 t1_isuroz9 wrote

I want to use an LSTM to predict a value that only is released at the end of a day. Say I have minute data for stock trades, and I want to forecast the highest trade of the day. So it is a forecasting problem until the point where the data is trending down, then it is more of a "what would highest trade be if our observed sample is this." Do I make all 1440 data points of a day have the same value? Or just the last one and I predict only the last value of the day? In the preprocessing phase and would appreciate insight.

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seiqooq t1_it3wkzy wrote

Try to think of this in terms of how you will use the model. It sounds like a day-trading model, correct me if I’m wrong. In this case, you’ll want to ask the question of “based on todays trading patterns, should I sell now, or is the peak still likely to come?”.

See if this helps your problem formulation and therefore your labeling.

As a side note, most models are not sophisticated enough to capture the extreme complexity of stock behavior. If this is your first foray into stock prediction, I’d recommend tempering expectations.

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