title: How to detect outliers in time series data of multiple individuals?
body: <p>I have time series data of a thousand people.</p>
  <p>I want to make a model that finds outliers in the time series of the remaining 200 people by training about 800 outlier labeled data.</p>
  <p>The data consists of a total of 4 columns.</p>
  <p>x1: id (User Classification)</p>
  <p>x2: datetime</p>
  <p>x3: value</p>
  <p>x4: outlier label</p>
  <p>Each user has a different time series interval.</p>
  <p>for example)</p>
  <p>Person 1: 2020.01.01 ~ 2020.11.31 (11 rows)</p>
  <p>Person 2: 2020.03.14 ~ 2020.08.11 (130 rows)</p>
  <p>Person 3: 2020.02.22 ~ 2020.07.29 (88 rows)...</p>
  <p>x4(label) is a 0,1 value indicating whether each x3(value) is an outlier at each time point.</p>
  <p>I'm at a loss as to how to train this data.</p>
  <p>I would appreciate it if you could tell me how to train this type of time series data as a model.</p>
  <p>P.S. If possible, I would appreciate it if you could provide a python or R code example.</p>