General Time Serie Anomalies Detection¶
GTSDetection object¶
Many differents models for anomaly detection.
List of models¶
IEQ_Detector
Mad_Detector
Variation_Detector
Period_Detector
Autoencoder_Detector
Wave_Detector
Use¶
Detect disruption in the seasonal shape for contextual anomalies:
from GTSFutur.GTSDetector import Wave_Detector,Period_Detector,Mad_Detector,Variation_Detector,Autoencoder_Detector,IEQ_Detector
detector=Period_Detector()
detector.fit(data)
anomaly=detector.detect(data,threshold=0.2)
detector.plot_anomalies(anomaly,data)
Detect points far from the average statical distribution:
from GTSFutur.GTSDetector import Wave_Detector,Period_Detector,Mad_Detector,Variation_Detector,Autoencoder_Detector,IEQ_Detector
detector=IEQ_Detector()
detector.fit(data)
#alpha a severity parameter
anomaly=detector.detect(data,alpha=1.96)
detector.plot_anomalies(anomaly,data)
Detect points far from the median:
from GTSFutur.GTSDetector import Wave_Detector,Period_Detector,Mad_Detector,Variation_Detector,Autoencoder_Detector,IEQ_Detector
detector=Mad_Detector()
detector.fit(data)
anomaly=detector.detect(data,alpha=0.6785)
detector.plot_anomalies(anomaly,data)
Detect important changes of values for measurements like mean,standard deviation and median on slidding windows:
from GTSFutur.GTSDetector import Wave_Detector,Period_Detector,Mad_Detector,Variation_Detector,Autoencoder_Detector,IEQ_Detector
detector=Variation_Detector(method="std")
detector.fit(data)
anomaly=detector.detect(data,threshold=0.5,windows_size=25)
detector.plot_anomalies(anomaly,data)
Detect peaks once the signal denoised:
from GTSFutur.GTSDetector import Wave_Detector,Period_Detector,Mad_Detector,Variation_Detector,Autoencoder_Detector,IEQ_Detector
detector=Wave_Detector()
detector.fit(data,threshold=0.3)
anomaly=detector.detect(data,alpha=10)
detector.plot_anomalies(anomaly,data)
Use a LSTM auto encoder to detect more complexe, global and contextual, anomalies:
from GTSFutur.GTSDetector import Wave_Detector,Period_Detector,Mad_Detector,Variation_Detector,Autoencoder_Detector,IEQ_Detector
detector=Autoencoder_Detector()
detector.fit(100,data,"My_directory_name")
anomaly=detector.detect(data,"My_directory_name",threshold=5)
detector.plot_anomalies(anomaly,data)
