Join GitHub today
GitHub is home to over 28 million developers working together to host and review code, manage projects, and build software together.Sign up
LSTM for time series prediction #2856
This is more of a conceptual clarification.
I have the following architecture :
The LSTM is able to make pretty accurate predictions as of now.
Note that the training data for each of the 3 weeks is almost similar in shape.
As you can see, the LSTM is still able to trace the unexpected peak in the graph which was never seen in the training data.
Any reason as to how the LSTM is able to do the above?
@hijoe320 Ok, So are we doing any thing wrong to end up with this?
The main problem we have is the "Testing Data" real-time we get only 1 time-step at a time. So they are tested on a batch-size = 1 and time-step = 1.
Anyways to overcome this "trap"?
We have trained Each Timestep as a sample.
Say lstm requires input of the form => nb_samples x tsteps x attributes
where a,b,c,d... is a time series data
10:01 am -> X = 1 x 1 x b and y = c goes into lstm..
10:02 am -> X = 1 x 1 x c and y = d goes into lstm.. And So on..
@sjayakum you can try:
hi there. May I know how to use LSTM for multiple step ahead forecasting? Assuming I would like to predict 7 days ahead outcome. I've been trying few days but seems to get stuck - how should i prepare the data, and and what to set for batch size and time steps. Thanks in advance!
I have the same problem while using LSTM to predict a complex time series. There is a delay between true value and predicted value and they have a simular shape. It seems that the network copies the last input value to generate an output value. Are there any solutions to this problems? I will be very grateful.