How to implement LSTM Time-series prediction using multi-features?
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Hello everyone,
I have the attached code and the attached data file here. I want to modify that code to proceed time-series prediction for 1 output using 5 inputs.
When I the training finishes I get the following error:
The prediction sequences are of feature dimension 1 but the input layer expects sequences of feature dimension 4.
Error in multi_lstmOMNI_noStand (line 110)
[net,YPred] = predictAndUpdateState(net,YTrain);
Can you please tell me how to fix it?
I appreciate your help.
5 个评论
OLUWAFEMI AJAYI
2020-1-29
Hello,
I am also having the same challenge using that code for time-series prediction for two input/one output. Please have you been able to fix the error?
Mohamed Nedal
2020-4-7
Marcelo Olmedo
2020-5-6
Hello people; I found the problem; the key is in the correct loading of data as the published documents say. I attach my code and used tables so you don't have problems to run it; I upload data from excel to train and test. I do not use standardized data. The model fits quite well. Cheers
Mohamed Nedal
2020-5-9
Mohamed Nedal
2020-7-3
采纳的回答
Marcelo Olmedo
2020-5-6
2 个投票
Hello people; I found the problem; the key is in the correct loading of data as the published documents say. I attach my code and used tables so you don't have problems to run it; I upload data from excel to train and test. I do not use standardized data. The model fits quite well. Cheers
4 个评论
Mohamed Nedal
2020-5-9
编辑：Mohamed Nedal
2020-5-9
Mohamed Nedal
2020-7-3
Song Decn
2021-4-27
Thnks!!
Asli
2021-5-21
Hi Marcelo, i am really new in this topic and i am trying to predict time series of Y depending on two external variables (X1 and X2). I have two questions. (i) Why don’t you use the “predictAndUpdateState” and why do you use the “predict” statement? Is it used for time series prediction? (ii) Why are the previous y values not input into this function? Only x values are input.
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