How to use LSTM to predict real time series?
I have tried to use this network to predict several time series including sin (t) and a real traffic flow dataset. I found that the prediction for sin is fine while the prediction for real dataset is just like shifting the last input value by one step.
Why are predictions from my LSTM neural network?
I am running an LSTM neural network in R using the keras package, in an attempt to do time series prediction of Bitcoin. The issue I’m running into is that while my predicted values seem to be reasonable, for some reason, they are “lagging” or “behind” the true values.
Is the prediction for sin a prediction error?
I found that the prediction for sin is fine while the prediction for real dataset is just like shifting the last input value by one step. I don’t know whether it’s a prediction error or the network doesn’t learn the pattern of the dataset at all.
When do I Change my prediction to 72 hours ahead?
When I change it to predicting 72 hours ahead, the prediction lag isn’t exactly 72 hours (just like it isn’t exactly 24 hours when I’m predicting that far ahead). However, the prediction lag noticeably increases/decreases when I increase/decrease how far ahead I’m trying to predict..
Is the number of timesteps in LSTM networks a limiting factor?
There is a general trend of increasing test RMSE as the number of time steps is increased. The expectation of increased performance with the increase of time steps was not observed, at least with the dataset and LSTM configuration used. This raises the question as to whether the capacity of the network is a limiting factor.
What is ” target delay ” in the context of LSTM?
Unidirectional RNN with one hidden layers containing 275 sigmoidal units, trained with target delays from 0 to 10 frames (RNN) can somebody explain how this works? the model sees a certain frame plus a few frames from the future (aka. delay) and then predicts the class for the original frame. Am I right?