Due to recent updates, all users are required to create an Altair One account to login to the RapidMiner community. Click the Register button to create your account using the same email that you have previously used to login to the RapidMiner community. This will ensure that any previously created content will be synced to your Altair One account. Once you login, you will be asked to provide a username that identifies you to other Community users. Email us at Community with questions.
Your help to enhance my RNN-LSTM solution
Sara_Almadi
Member Posts: 1 
Learner I
in Help
Hello everyone,
In the first place, I would like to thank you for this helpful RapidMiner community platform that helped me a lot in solving many issues in developing this early prediction model. Thus, I am seeking the RapidMiner community's advice regarding my process.
Actually, I am trying to develop an early prediction model for predicting the value of column D using A, B, and C features. As my data is sequential, I tried two different preprocessing procedures to preprocess the dataset and train the deep learning model for early prediction.
In the first process, I used sequence and batch procedures. I tried to loop through the sequences and create 30% of the sequences for each batch. Then I replaced the final score (the final value) of each batch in the D column at the end of the 30% (for example, if I had 60 sequences in one batch, I sliced out the first 20 sequences and placed the final value of column D (in row 60) at row 20 in column D). After the data preprocessing, I used cross-validation to train the deep learning model.
On the other hand, in the second process, I used the window operator. I looped through the values of the dataset and created a 30% time step window for each batch. Then I placed the final value of each batch as a label for the 30% window. Then I used cross-validation to train the deep learning model.
Actually, I am trying to develop an early prediction model for predicting the value of column D using A, B, and C features. As my data is sequential, I tried two different preprocessing procedures to preprocess the dataset and train the deep learning model for early prediction.
In the first process, I used sequence and batch procedures. I tried to loop through the sequences and create 30% of the sequences for each batch. Then I replaced the final score (the final value) of each batch in the D column at the end of the 30% (for example, if I had 60 sequences in one batch, I sliced out the first 20 sequences and placed the final value of column D (in row 60) at row 20 in column D). After the data preprocessing, I used cross-validation to train the deep learning model.
On the other hand, in the second process, I used the window operator. I looped through the values of the dataset and created a 30% time step window for each batch. Then I placed the final value of each batch as a label for the 30% window. Then I used cross-validation to train the deep learning model.
I attached both processes, as well as a sample of my dataset. Therefore, I seek your advice regarding my concerns, which are:
- Is there any overall advice regarding these two processes?
- Is it allowed to use the windowing approach for preprocessing sequential data, even though it is often used for date and time series data?
- During model training using both processes, I faced an issue with the cross-validation performance results. I got a low squared correlation value; however, the relative error and the RMSE values were good. Is there any justification for this issue?
- My issue with the two processes is that I usually get a low squared correlation value when I train the RNN model or LSTM. Is there any advice that could help me enhance the performance results in terms of RMSE, RE, and squared correlation?
- Is there any advice that can help me handle the issue of getting good performance results using unseen data but bad results in the cross-validation performance results?
0
