def calculateMovingAverageParameters(series,
window=0,
threshold=2.0,
calculateScore=False):
if window == 0:
window = len(series)
rolling_mean = series.rolling(window=window).mean()
mae = mean_absolute_error(series.iloc[window - 1:, :],
rolling_mean.iloc[window - 1:, :])
deviation = np.std(series.iloc[window - 1:, :] -
rolling_mean.iloc[window - 1:, :])
if (calculateScore):
mape = mean_absolute_percentage_error(
series.iloc[window - 1:, :], rolling_mean.iloc[window - 1:, :])
logging.info("Mean absolute error is " + str(mae) +
", Mean absolute percentage_error is " + str(mape) + "%")
llower_bound = rolling_mean - (mae + threshold * deviation)
hupper_bound = rolling_mean + (mae + threshold * deviation)
mean_upper, std_upper = calculateHistoricalParameters(hupper_bound)
mean_lower, std_lower = calculateHistoricalParameters(llower_bound)
#add other models for different use case
other_models = {}
other_models[MEAN_UPPER] = mean_upper
other_models[MEAN_LOWER] = mean_lower
other_models[STD_UPPER] = std_upper
other_models[STD_LOWER] = std_lower
other_models[PREDIT_UPPERS] = llower_bound
other_models[PREDIT_LOWERS] = hupper_bound
# return hlower_bound.y.values[len(llower_bound)-1], hupper_bound.y.values[len(hupper_bound)-1], other_models
return llower_bound.y.values[len(llower_bound) -
1], hupper_bound.y.values[len(hupper_bound) -
1]
'''
def detectDoubleExponentialSmoothingAnomalies(series, y, alpha, beta, threshold=2, mae = 0, deviation= 0, bound=IS_UPPER_BOUND, calculateScore=False, debug=False, returnAnomaliesOnly= True):
yhat = double_exponential_smoothing(y, alpha, beta)
# calculate confidence intervals for smoothed values
if( mae==0 and deviation ==0 ):
mae = mean_absolute_error(y, yhat)
if calculateScore==True or debug == True:
mape = mean_absolute_percentage_error(y, yhat)
logging.info("Mean absolute error is "+str(mae)+", Mean absolute percentage_error is "+str(mape)+"%")
if (mae ==0 and deviation == 0 ):
deviation = np.std(y - yhat)
lower_bond = yhat - (mae + threshold * deviation)
upper_bond = yhat + (mae + threshold * deviation)
if debug == True:
logging.info(lower_bond)
logging.info(upper_bond)
logging.info(y)
ts=[]
adata=[]
anomalies=[]
myshape = series.shape
nrow = myshape[0]
i=0
for i in range(nrow):
isAnomaly = False
if (not returnAnomaliesOnly):
ts.append(series.index[i])
adata.append(series.iloc[i,0])
if bound==IS_UPPER_BOUND:
if y[i] > upper_bond[i]:
if returnAnomaliesOnly:
ts.append(series.index[i])
adata.append(y[i])
isAnomaly = True
elif bound==IS_LOWER_BOUND:
if y[i] < lower_bond[i]:
if returnAnomaliesOnly:
ts.append(series.index[i])
adata.append(y[i])
isAnomaly = True
else:
if y[i] > upper_bond[i] or y[i] < lower_bond[i]:
if returnAnomaliesOnly:
ts.append(series.index[i])
adata.append(y[i])
isAnomaly = True
if returnAnomaliesOnly:
if isAnomaly:
anomalies.append(True)
else:
anomalies.append(isAnomaly)
#return mae, deviation, addHeader(ts,adata)
if calculateScore:
return mae, deviation, ts, adata, anomalies, mape
return mae, deviation, ts, adata, anomalies
def trainMovingAverageParameters(series, windowlist, calculateScore=True ):
minmape = -1
windowno = 0
for window in windowlist:
rolling_mean = series.rolling(window=window).mean()
mape = mean_absolute_percentage_error(series.iloc[window-1:,:], rolling_mean.iloc[window-1:,:])
if (minmape == -1):
minmape = mape
windowno = window
elif (minmape> mape):
minmape = mape
windowno = window
return windowno
def calculateExponentialSmoothingParameters(series, alpha=0.9, threshold=2, calculateScore=False):
y= series.y
yhat = exponential_smoothing(y.values, alpha)
# calculate confidence intervals for smoothed values
mae = mean_absolute_error(y.values, yhat)
deviation = np.std(y.values - yhat)
mape = 0
if calculateScore:
mape = mean_absolute_percentage_error(y.values, yhat)
logging.info("Mean absolute error is "+str(mae)+", Mean absolute percentage_error is "+str(mape)+"%")
hlower_bound = yhat - (mae + threshold * deviation)
hupper_bound = yhat + (mae + threshold * deviation)
if calculateScore:
return hlower_bound[len(hlower_bound)-1], hupper_bound[len(hupper_bound)-1],mape
return hlower_bound[len(hlower_bound)-1], hupper_bound[len(hupper_bound)-1]
def trainDoubleExponentialSmoothingParameters(y, alphas, betas):
minmap = -1
alpha = 0
beta = 0
for a in alphas:
for b in betas:
yhat = double_exponential_smoothing(y, a, b)
mape = mean_absolute_percentage_error(y, yhat)
if (minmap == -1):
minmap = mape
alpha = a
beta = b
elif minmap > mape:
minmap = mape
alpha = a
beta = b
return alpha, beta
def calculateMovingAverageParameters(series,
window=0,
threshold=2.0,
calculateScore=False):
if window == 0:
window = len(series)
rolling_mean = series.rolling(window=window).mean()
mae = mean_absolute_error(series.iloc[window - 1:, :],
rolling_mean.iloc[window - 1:, :])
deviation = np.std(series.iloc[window - 1:, :] -
rolling_mean.iloc[window - 1:, :])
if (calculateScore):
mape = mean_absolute_percentage_error(
series.iloc[window - 1:, :], rolling_mean.iloc[window - 1:, :])
logging.info("Mean absolute error is " + str(mae) +
", Mean absolute percentage_error is " + str(mape) + "%")
hlower_bound = rolling_mean - (mae + threshold * deviation)
hupper_bound = rolling_mean + (mae + threshold * deviation)
return hlower_bound.y.values[len(hlower_bound) -
1], hupper_bound.y.values[len(hupper_bound) -
1]
'''
def retrieveHW_MAPE(y, model):
error = mean_absolute_percentage_error(y.values, model.result[:len(y)])
return error
