| ... | ... | @@ -107,4 +107,6 @@ Let's take our dataset and notebook as an example. We have time vs. load data, c |
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Next, we need to decide how much past is relevant to make predictions about the future. To make it easier to plot, I will take it as 3 here. Our data representation becomes (1, 3, 4) for one training instance. At this point, I decided to make predictions one hour ahead, so I can use many-to-one approach (3 past observations of 4 features will give one prediction).
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Next, I decided to use 2 RNN layers, followed by and 2 MLP layers. RNN layers have 6 nodes, while MLP layers have 3 and 1 node respectively. Note that these are custom selections, except the last MLP layer (since this is a regression task, I will have one linear node at the end to give me continuous values).
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