How to improve the result of "Time Series Forecasting Using Deep Learning" ?
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I am working on "Time Series Forecasting Using Deep Learning." (https://www.mathworks.com/help/deeplearning/examples/time-series-forecasting-using-deep-learning.html?searchHighlight=predictAndUpdateState&s_tid=doc_srchtitle)
The result of the prediction is not satisfactory compared to what I expected.
How can I improve the result of prediction?
For instance, what options can I change?
It may improve if I use more data, but it is limited.
I changed epoc number, initial learning step size, training data number, etc; nonetheless, the result is not satisfactory
Please let me know if there are any ways to improve the result for prediction. Thanks
Kritika Bansal on 31 Jul 2019
You can try tuning the parameters like ‘MiniBatchSize’, ‘MaxEpochs’ and ‘Solver’ to train the network well. Also try to tune the parameters within a particular ‘Solver’ like tuning the value of ‘Momentum’ for ‘sgdm’. Refer to the link below to explore more such options:
Jaechan Lim on 2 Aug 2019
I changed solverName from "Adam" to "rmsprop" and somehow it worked better.
I also needed to adjust the values of "InitialLearnRate".
The tuning process is not easy, but thanks, anyway.