異常検知①
Anomaly Detection Toolkit (ADTK)を使った異常検知をしてみます。 オリジナルデータはNumenta Anomaly Benchmarkのものを使用しています。
import pandas as pd
s_train = pd.read_csv(
"./training.csv", index_col="timestamp", parse_dates=True, squeeze=True
)
from adtk.data import validate_series
s_train = validate_series(s_train)
print(s_train)
timestamp
2014-04-01 00:00:00 18.090486
2014-04-01 00:05:00 20.359843
2014-04-01 00:10:00 21.105470
2014-04-01 00:15:00 21.151585
2014-04-01 00:20:00 18.137141
...
2014-04-14 23:35:00 18.269290
2014-04-14 23:40:00 19.087351
2014-04-14 23:45:00 19.594689
2014-04-14 23:50:00 19.767817
2014-04-14 23:55:00 20.479156
Freq: 5T, Name: value, Length: 4032, dtype: float64
s_train = pd.read_csv("./training.csv", index_col="timestamp", parse_dates=True, squeeze=True)
from adtk.visualization import plot
plot(s_train)
[<Axes: >]
異常検知手法の比較
SeasonalADを用いた異常検知を行います。他の手法はDetectorを参照してください。
import matplotlib.pyplot as plt
from adtk.detector import (
AutoregressionAD,
InterQuartileRangeAD,
LevelShiftAD,
PersistAD,
SeasonalAD,
)
model_dict = {
"LevelShiftAD": LevelShiftAD(window=5),
"SeasonalAD": SeasonalAD(),
"PersistAD": PersistAD(c=3.0, side="positive"),
"InterQuartileRangeAD": InterQuartileRangeAD(c=1.5),
"AutoregressionAD": AutoregressionAD(n_steps=7 * 2, step_size=24, c=3.0),
}
for model_name, model in model_dict.items():
anomalies = model.fit_detect(s_train)
plot(s_train, anomaly=anomalies, anomaly_color="red", anomaly_tag="marker")
plt.title(model_name)
plt.show()
