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
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Kritika Bansal
2019-7-31
Hi,
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:
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Jaechan Lim
2019-8-2
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.
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