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I am using STL to decompose my time series data in Season, trend and residual and then by applying this(see below) on residual. I am detecting the anomaly


    resid_mu = resid.mean()
    resid_dev = resid.std()
    
    //anything outside lower and upper limit is anamoly
    lower = resid_mu - 3*resid_dev
    upper = resid_mu + 3*resid_dev

If I do this on 1 year of data it is giving a good result but now if I get new data say for 1 new day how can I use older decomposition data so that I have to process for 1 day only and not for 1 year+1 day.

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  • $\begingroup$ What is the minimum amount of data to detect seasonality? Is one day enough? $\endgroup$ Jun 28 at 10:05
  • $\begingroup$ What is the minimum amount of data to detect seasonality? 1 week Is one day enough? I am getting incremental data daily. $\endgroup$ Jun 29 at 10:54
  • $\begingroup$ In order to make a prediction on the next day (or the next several days), your model should consider several previous days, months or even years, depending on seasonality period. You cannot make a prediction based on a single value, you need to be based on enough values (maybe values on a year for instance) to detect the seasonal behaviours. $\endgroup$ Jun 29 at 11:36

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