[P] Subsequence to sequence prediction LSTM / stacked LSTM
![[P] Subsequence to sequence prediction LSTM / stacked LSTM](https://b.thumbs.redditmedia.com/gWsv1HAQJdjVkBnUXMjLidwWsjKlqeoIPF8r8-eRTLI.jpg "[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: https://i.redd.it/b9x7k87nw3631.png 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 |
