Comparison between approaches for timeseries anomaly detection












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$begingroup$


After various days of research, I could take a global picture of the existing methods to perform anomaly detection on time series, namely:




  • Forecasting with Deep Learning. Eg. RADM or LSTM model


  • Forecasting without Deep Learning. Eg. Seasonal ARIMA + Kalman Filters


  • Denoising fixed-length windows with autoencoders (Deep Learning approach). Eg. MAD-GAN, CNN/LSTM autoencoders, variational autoencoders, etc.


  • Denoising without deep-learning. Eg. Applying filters such as Kalman or Hodrick Prescott, and test if the deviation of the predicted with the original timeseries is under a threshold.



Maybe there are even more methods that are not classificable into this screenshot.



My question is, which approach suits better the need of developing a timeseries anomaly system which:




  • Detects anomalies in an univariate way, but allows for multivariate posterior integration.

  • Is suitable for online data-streaming

  • Is capable of learning data distributions holding more than one seasonality.

  • [Plus] Might allow for semi-supervised improvement at any step.










share|improve this question









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    0












    $begingroup$


    After various days of research, I could take a global picture of the existing methods to perform anomaly detection on time series, namely:




    • Forecasting with Deep Learning. Eg. RADM or LSTM model


    • Forecasting without Deep Learning. Eg. Seasonal ARIMA + Kalman Filters


    • Denoising fixed-length windows with autoencoders (Deep Learning approach). Eg. MAD-GAN, CNN/LSTM autoencoders, variational autoencoders, etc.


    • Denoising without deep-learning. Eg. Applying filters such as Kalman or Hodrick Prescott, and test if the deviation of the predicted with the original timeseries is under a threshold.



    Maybe there are even more methods that are not classificable into this screenshot.



    My question is, which approach suits better the need of developing a timeseries anomaly system which:




    • Detects anomalies in an univariate way, but allows for multivariate posterior integration.

    • Is suitable for online data-streaming

    • Is capable of learning data distributions holding more than one seasonality.

    • [Plus] Might allow for semi-supervised improvement at any step.










    share|improve this question









    $endgroup$















      0












      0








      0





      $begingroup$


      After various days of research, I could take a global picture of the existing methods to perform anomaly detection on time series, namely:




      • Forecasting with Deep Learning. Eg. RADM or LSTM model


      • Forecasting without Deep Learning. Eg. Seasonal ARIMA + Kalman Filters


      • Denoising fixed-length windows with autoencoders (Deep Learning approach). Eg. MAD-GAN, CNN/LSTM autoencoders, variational autoencoders, etc.


      • Denoising without deep-learning. Eg. Applying filters such as Kalman or Hodrick Prescott, and test if the deviation of the predicted with the original timeseries is under a threshold.



      Maybe there are even more methods that are not classificable into this screenshot.



      My question is, which approach suits better the need of developing a timeseries anomaly system which:




      • Detects anomalies in an univariate way, but allows for multivariate posterior integration.

      • Is suitable for online data-streaming

      • Is capable of learning data distributions holding more than one seasonality.

      • [Plus] Might allow for semi-supervised improvement at any step.










      share|improve this question









      $endgroup$




      After various days of research, I could take a global picture of the existing methods to perform anomaly detection on time series, namely:




      • Forecasting with Deep Learning. Eg. RADM or LSTM model


      • Forecasting without Deep Learning. Eg. Seasonal ARIMA + Kalman Filters


      • Denoising fixed-length windows with autoencoders (Deep Learning approach). Eg. MAD-GAN, CNN/LSTM autoencoders, variational autoencoders, etc.


      • Denoising without deep-learning. Eg. Applying filters such as Kalman or Hodrick Prescott, and test if the deviation of the predicted with the original timeseries is under a threshold.



      Maybe there are even more methods that are not classificable into this screenshot.



      My question is, which approach suits better the need of developing a timeseries anomaly system which:




      • Detects anomalies in an univariate way, but allows for multivariate posterior integration.

      • Is suitable for online data-streaming

      • Is capable of learning data distributions holding more than one seasonality.

      • [Plus] Might allow for semi-supervised improvement at any step.







      time-series anomaly-detection online-learning






      share|improve this question













      share|improve this question











      share|improve this question




      share|improve this question










      asked 16 mins ago









      freesoulfreesoul

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