Univariate detector#
KNNCAD Detector#
from streamad.util import StreamGenerator, UnivariateDS, plot
from streamad.model import KNNDetector
ds = UnivariateDS()
stream = StreamGenerator(ds.data)
model = KNNDetector()
scores = []
for x in stream.iter_item():
score = model.fit_score(x)
scores.append(score)
data, label, date, features = ds.data, ds.label, ds.date, ds.features
plot(data=data,scores=scores,date=date,features=features,label=label)
Spot Detector#
from streamad.util import StreamGenerator, UnivariateDS, plot
from streamad.model import SpotDetector
ds = UnivariateDS()
stream = StreamGenerator(ds.data)
model = SpotDetector(back_mean_len=10)
scores = []
for x in stream.iter_item():
score = model.fit_score(x)
scores.append(score)
data, label, date, features = ds.data, ds.label, ds.date, ds.features
plot(data=data,scores=scores,date=date,features=features,label=label)
Spectral Residual Detector#
from streamad.util import StreamGenerator, UnivariateDS, plot
from streamad.model import SRDetector
ds = UnivariateDS()
stream = StreamGenerator(ds.data)
model = SRDetector()
scores = []
for x in stream.iter_item():
score = model.fit_score(x)
scores.append(score)
data, label, date, features = ds.data, ds.label, ds.date, ds.features
plot(data=data,scores=scores,date=date,features=features,label=label)
Z-score Detector#
from streamad.util import StreamGenerator, UnivariateDS, plot
from streamad.model import ZScoreDetector
ds = UnivariateDS()
stream = StreamGenerator(ds.data)
model = ZScoreDetector()
scores = []
for x in stream.iter_item():
score = model.fit_score(x)
scores.append(score)
data, label, date, features = ds.data, ds.label, ds.date, ds.features
plot(data=data,scores=scores,date=date,features=features,label=label)
One-class SVM Detector#
from streamad.util import StreamGenerator, UnivariateDS, plot
from streamad.model import OCSVMDetector
ds = UnivariateDS()
stream = StreamGenerator(ds.data)
model = OCSVMDetector()
scores = []
for x in stream.iter_item():
score = model.fit_score(x)
scores.append(score)
data, label, date, features = ds.data, ds.label, ds.date, ds.features
plot(data=data,scores=scores,date=date,features=features,label=label)
Median Absolute Deviation Detector#
from streamad.util import StreamGenerator, UnivariateDS, plot
from streamad.model import MadDetector
ds = UnivariateDS()
stream = StreamGenerator(ds.data)
model = MadDetector()
model.detrend
scores = []
for x in stream.iter_item():
score = model.fit_score(x)
scores.append(score)
data, label, date, features = ds.data, ds.label, ds.date, ds.features
plot(data=data,scores=scores,date=date,features=features,label=label)
Seasonal Arima Detector#
from streamad.util import StreamGenerator, UnivariateDS, plot
from streamad.model import SArimaDetector
ds = UnivariateDS()
stream = StreamGenerator(ds.data)
model = SArimaDetector()
scores = []
for x in stream.iter_item():
score = model.fit_score(x)
scores.append(score)
data, label, date, features = ds.data, ds.label, ds.date, ds.features
plot(data=data,scores=scores,date=date,features=features,label=label)