Answers.Body: <p>After researching with more specific terms it appears the topic has been discussed and has to do with &quot;forecastability&quot; of a time series. As far as I can understand a test 'for randomness' is critical, to establish if the input feature (in this case, network I/O) is basically 'noise' in respect with the output (cpu load%).</p>
  
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Answers.CreationDate: 2023-10-28T11:16:39.370Z
Answers.LastActivityDate: 2023-10-28T11:16:39.370Z
Answers.UserDisplayName: jet
Body: <p>I am a beginner in ML, I came across the below scenario. I wonder if I could use ML (and which kind) and what would be the the steps needed for the pre-processing of a time-series. So first (1)how to reach Go/NoGo decision, and then (2)how to select a suitable processing steps.</p>
  <p>The conceptual scenario :</p>
  <ul>
  <li>I would like to predict the CPU load of a laptop (in %), as function of network I/O (samples taken every 5s, in Mbps in and out, for all interfaces wifi/eth, etc).</li>
  <li>Assume I have history data collected for both (CPU load%) and (networkIO mbps), for both training and test.</li>
  <li>Assume the laptop is getting used during the day(whatever task) but it's left on/idle during the night just to collect continuous data</li>
  <li>So finally, if I inject/or generate a certain amount of traffic (like downloading a movie), would like to predict the corresponding CPU load</li>
  </ul>
  <p>The context that generating my question:</p>
  <ul>
  <li>Obviously there are a ton of other variables influencing the CPU load, like the disk I/O, encryption, the user tasks the laptop is performing, as well the OS background/virus, etc.</li>
  <li>As the tasks are not identified and they might occur randomly I would expect the relation between networkIO feature and the cpu-load% to be quasi random/no correlation</li>
  </ul>
  <p>So - Intuitively I would expect the problem to be insolvable (one can't predict CPU load% only as function of the network IO).</p>
  <p>But the more specific Q is - which data exploration techniques should be applied in order to &quot;prove&quot; that CPU load% can't be predicted only as function of the network IO ?</p>
  <p>Searched SE, I haven't found a complete answer while I admit for not having RTFM.</p>
  
Tags: <machine-learning>
Title: ML predicting output based on single feature
UserDisplayName: jet
ThreadText: # ML predicting output based on single feature
  
  (Asked by: username_0 on 2023-10-27)
  
  I am a beginner in ML, I came across the below scenario. I wonder if I could use ML (and which kind) and what would be the the steps needed for the pre-processing of a time-series. So first (1)how to reach Go/NoGo decision, and then (2)how to select a suitable processing steps.
  
  The conceptual scenario :
  
  * I would like to predict the CPU load of a laptop (in %), as function of network I/O (samples taken every 5s, in Mbps in and out, for all interfaces wifi/eth, etc).
  * Assume I have history data collected for both (CPU load%) and (networkIO mbps), for both training and test.
  * Assume the laptop is getting used during the day(whatever task) but it's left on/idle during the night just to collect continuous data
  * So finally, if I inject/or generate a certain amount of traffic (like downloading a movie), would like to predict the corresponding CPU load
  
  The context that generating my question:
  
  * Obviously there are a ton of other variables influencing the CPU load, like the disk I/O, encryption, the user tasks the laptop is performing, as well the OS background/virus, etc.
  * As the tasks are not identified and they might occur randomly I would expect the relation between networkIO feature and the cpu-load% to be quasi random/no correlation
  
  So - Intuitively I would expect the problem to be insolvable (one can't predict CPU load% only as function of the network IO).
  
  But the more specific Q is - which data exploration techniques should be applied in order to "prove" that CPU load% can't be predicted only as function of the network IO ?
  
  Searched SE, I haven't found a complete answer while I admit for not having RTFM.
  
  **Tags:** machine-learning
  
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  ## 1 answers
  
  ### Answer by username_0 on 2023-10-28. Score: 1
  
  After researching with more specific terms it appears the topic has been discussed and has to do with "forecastability" of a time series. As far as I can understand a test 'for randomness' is critical, to establish if the input feature (in this case, network I/O) is basically 'noise' in respect with the output (cpu load%).
  
  
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