Answers.Body: <p>welcome to the community.</p>
  <p>Let me imagine this problem to be a classification task depending upon the smartwatch accelerometer and gyroscope data collected over time (time series);</p>
  <p>Say we have a target variable where we need to predict what activity the human is doing;</p>
  <p>You can proceed to input these numeric data normally in your algorithm (either a DNN or some ML algorithm) and let the algorithm figure out what the target variable would be. Don't forget to standardize your features!</p>
  <p>To proceed with these kinds of problems, I always suggest doing some EDA, i.e. checking how these 7 features vary depending on the target class. For example, on the smartphone data (accelerometer) for an idle person (sitting or lying)  is drastically different from a dynamic person (walking, running).</p>
  <p>If you see these patterns already, you can expect your algorithm to perform reasonably well. Good luck :)</p>
  
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Answers.CreationDate: 2024-08-30T06:58:20.390Z
Answers.LastActivityDate: 2024-08-30T06:58:20.390Z
Answers.UserDisplayName: Suvo
Body: <p>I have multiple independent simulated time series data (for physic experiment). Each time series has t = 0 to 10000, and 7 features with 1 target. I want to build a model that I can train on all the simulated data and make prediction on dataset Test which has only 7 features and the Time column (t=0 to 10000) without the Target. (Completely independent case of the training set)</p>
  <p>I tried to think this as a time-series problem since target is time dependent but my goal here seems not? Because if I train a time series model, my Test set needs a historical part, but in my case,my data does not.</p>
  
Tags: <time-series>
Title: How should I see this problem as? Normal or Time Series?
UserDisplayName: Khoa Tan Dang
ThreadText: # How should I see this problem as? Normal or Time Series?
  
  (Asked by: username_0 on 2024-08-29)
  
  I have multiple independent simulated time series data (for physic experiment). Each time series has t = 0 to 10000, and 7 features with 1 target. I want to build a model that I can train on all the simulated data and make prediction on dataset Test which has only 7 features and the Time column (t=0 to 10000) without the Target. (Completely independent case of the training set)
  
  I tried to think this as a time-series problem since target is time dependent but my goal here seems not? Because if I train a time series model, my Test set needs a historical part, but in my case,my data does not.
  
  **Tags:** time-series
  
  
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  ## 1 answers
  
  ### Answer by username_1 on 2024-08-30. Score: 1
  
  welcome to the community.
  
  Let me imagine this problem to be a classification task depending upon the smartwatch accelerometer and gyroscope data collected over time (time series);
  
  Say we have a target variable where we need to predict what activity the human is doing;
  
  You can proceed to input these numeric data normally in your algorithm (either a DNN or some ML algorithm) and let the algorithm figure out what the target variable would be. Don't forget to standardize your features!
  
  To proceed with these kinds of problems, I always suggest doing some EDA, i.e. checking how these 7 features vary depending on the target class. For example, on the smartphone data (accelerometer) for an idle person (sitting or lying) is drastically different from a dynamic person (walking, running).
  
  If you see these patterns already, you can expect your algorithm to perform reasonably well. Good luck :)
  
  
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