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[P] Subsequence to sequence prediction LSTM / stacked LSTM

[P] Subsequence to sequence prediction LSTM / stacked LSTM

Hi :),

I’m currently on a project, where I’m trying to predict the next value of a sequence.

The data looks as follows:

y: the value to predict is captured ones a day.

x: there is an input sequence of around 2000 timesteps for every day

I would like to predict the next day’s value of y, i.e. y_{t+1}. However y_{t+1} is assumed to be not only dependent on the values of x but also on the history of y, i.e. y_t, y_{t-1}, y_{t-n}. I’m wondering how I could implement this idea in a LSTM-structure.

My idea is a network that looks like that:

Does that make sense or am I on the wrong track there?

How would you implement such a model in Keras? My idea was to make a network that looks like: x -> TimeDistributed(LSTM1) -> LSTM2 -> y

submitted by /u/cptn_iglo
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.