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I need to predict the performance (CPI cycles-per-instruction) of 90 machines for the next hour (or day). Each machine has a thousand records (e.g. CPU and memory usage).

Currently, I am using a neural network with one hidden layer for this task. I have 9 inputs (features), 23 neurons in the hidden layer, and one output. I am using the Levenberg-Marquardt algorithm. Examples of the inputs (or features) are the CPU and memory capacity and usage, and the machine_id. and Output is performance. I have 90 machines. Currently, I get an MSE of $0.1$ and an R of $0.80$.

My dataset consist of 30 days. I trained my network for the first 29 days, and I use day 30 to test.

I have been advised to use deep learning to have more flexibility and improve the MSE and R results. Could deep learning be helpful in this case? If yes, which deep learning model could I use to improve the results?

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    $\begingroup$ No. Deep Learning efficient for big networks and samples of extremely high dimensions. It don't make sense to use it for 9 element vectors as samples. $\endgroup$ – mirror2image Dec 12 '19 at 7:10
  • $\begingroup$ It's highly plausible that you don't need anything neural. For model selection, the following image is always a good rule of thumb. https://i.stack.imgur.com/Y8jAg.png. $\endgroup$ – MatthewTuby Dec 12 '19 at 7:30

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