General Time Serie Prediction and Anomalies Detection
Exemple of LSTM + DECOMPOSITION prédiction :
Full documentation available here : https://gtsfutur.readthedocs.io/en/latest/index.html .
Summary
-GSFutur object
-Use case prediction
-Results prediction
-Use case anomalies detection
-Results anomalies detection
GTSPredictor object
This object simplify prediction by hidding all the paramaters for the user.
Let's focus on the most important methods :
-fit()
-predict()
-retrain()
-prediction_eval()
-load_models()
Install
pip install GTSFutur
Use prediction
Example use case (The dataframe needs to have a columns named "time" and one named "y" in order to work)
my dataframe (df) is like below and have a 200 points seasonal pattern :
"time","y"
"1749-01",58.0
"1749-02",62.6
"1749-03",70.0
...
Code for prediction 365 steps ahead
from GTSFutur.GTSPredictor import GTSPredictor
model=GTSPredictor()
model.fit(df,look_back=400,freq_period=200,directory="My_directory_name")
prediction,lower,upper=model.predict(steps=365)Code for prediction 365 steps ahead using the user interface
from GTSFutur.GTSPredictor import GTSPredictor
model=GTSPredictor()
model.fit_with_UI(df,directory="My_directory_name")
prediction,lower,upper=model.predict(steps=365)This will open a matplotlib figure and you will be able to select the seasonal pattern with the mouse
plot
model.plot_prediction(df,prediction,lower,upper)reuse a saved model
model=model.reuse(df,directory="My_directory_name")Retrain the model (on new data for example in order to do incremental learning)
# before the model need to be loaded either it's just after the first training or used reuse function
model=model.retrain(df)Results prediction
Theses are "out of the box" results. The only parameter to determine was the size of the seasonal pattern which is easy to find as he can be determine through visual inspection
Use anomalies detection
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 with wavelet decomposition:
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)Results anomalies detection
