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Closed Loop Network Step-Ahead Prediction Network The function CLOSELOOP replaces the feedback input with a direct connection from the output layer. Step-Ahead Prediction Network also known as removedelay function helps to remove delay to neural network’s response In Closed loop networks, its own predictions become the feedback inputs. targets with a delay ...


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This is a question of time series forecasting, since your numbers form a sequence. You may want to take a look at the "forecasting" tag at CrossValidated. If you have only 700 data points, ML/AI methods will likely not be very useful. Whatever you do, I would recommend you benchmark your chosen method against very simple approaches, like the ...


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Since you only have only 700 observations, I would not try a deep learning approach. I think it is very unlikely that any Deep Learning approach will learn a non-obvious relationship with that little data. What you could try is create a set of features based on lags. Create a feature, that is lagged by 1, by 2, by 3, and so on. Also moving average of lagged ...


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I guess the most "suitable" approach is to look up research papers on ML/AI/Stats based methods on bipolar disorder mood swings prediction/regression etc. Focus on the abstract, intro/related works and conclusion. Find out why the method is proposed, what the well-known approaches are, what the intuition for the proposed methods are. Find out the ...


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As all you have is a series of numbers, you should try using a sequence model. I suggest you look into RNNs and in particular LSTMs. Of course this is assuming despite the lack of "obvious patterns", there are some kind of hidden patterns in your data. If not, what you have is not very different than random walk in 3 dimensions - which makes the ...


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