def __init__(self):
self.iforest_obj = isolation_forest.IForest()
self.ewma_obj = ewma.Ewma()
self.polynomial_obj = polynomial_interpolation.PolynomialInterpolation(
)
self.statistic_obj = statistic.Statistic()
self.supervised_obj = xgboosting.XGBoosting()
def predict(self, X, window=DEFAULT_WINDOW):
"""
Predict if a particular sample is an outlier or not.
:param X: the time series to detect of
:param type X: pandas.Series
:param: window: the length of window
:param type window: int
:return: 1 denotes normal, 0 denotes abnormal
"""
ewma_obj = ewma.Ewma(self.alpha, self.coefficient)
ewma_ret = ewma_obj.predict(X)
if ewma_ret == 1:
result = 1
else:
polynomial_obj = polynomial_interpolation.PolynomialInterpolation(
self.threshold, self.degree)
polynomial_ret = polynomial_obj.predict(X, window)
result = polynomial_ret
return result