In [1]:
library(AnomalyDetection)
In [2]:
data(raw_data)
head(raw_data)
Out[2]:
timestampcount
11980-09-25 14:01:00182.478
21980-09-25 14:02:00176.231
31980-09-25 14:03:00183.917
41980-09-25 14:04:00177.798
51980-09-25 14:05:00165.469
61980-09-25 14:06:00181.878
In [3]:
res = AnomalyDetectionTs(raw_data, max_anoms=0.02, direction='both', plot=TRUE)
res$plot
# We observe that the input time series experiences both positive and negative anomalies.
In [6]:
# If interested in only yesterday's data
res = AnomalyDetectionTs(raw_data, max_anoms=0.02, direction='both', only_last="day", plot=TRUE)
res$plot
In [4]:
library(BreakoutDetection)
data(Scribe)
res = breakout(Scribe, min.size=24, method='multi', beta=.001, degree=1, plot=TRUE)
res$plot