Time series anomaly detection in Python
Project description
pyanomaly
Conjunto de algoritmos para detectar anomalias em Series Temporais.
Instalação
pip install pyanomaly
Como usar
Iremos realizar os testes no dataset contendo temperaturas diarias da cidade de Melbourne.
dataset: https://raw.githubusercontent.com/jbrownlee/Datasets/master/daily-min-temperatures.csv
# data
import numpy as np
import pandas as pd
# plot
import matplotlib.pyplot as plt
import seaborn as sns; sns.set()
df = pd.read_csv('./dados/daily-min-temperatures.csv', parse_dates=['Date'])
df.set_index('Date', inplace=True)
print(df.head(5).T)
Date 1981-01-01 1981-01-02 1981-01-03 1981-01-04 1981-01-05
Temp 20.7 17.9 18.8 14.6 15.8
df.plot(figsize=(8, 4));
Mad
mad = MAD()
mad.fit(df['Temp'])
outliers = mad.fit_predict(df['Temp'])
outliers.head()
Date
1981-01-15 25.0
1981-01-18 24.8
1981-02-09 25.0
1982-01-17 24.0
1982-01-20 25.2
Name: Temp, dtype: float64
fig, ax = plt.subplots(1, 1, figsize=(12, 6))
sns.lineplot(x=df.index, y=df['Temp'], ax=ax)
sns.scatterplot(x=outliers.index, y=outliers,
color='r', ax=ax)
plt.title('Zscore Robusto', fontsize='large');
Tukey
tu = Tukey()
tu.fit(df['Temp'])
outliers = tu.predict(df['Temp'])
outliers.head()
Date
1981-01-15 25.0
1981-01-18 24.8
1981-02-09 25.0
1982-01-17 24.0
1982-01-20 25.2
Name: Temp, dtype: float64
fig, ax = plt.subplots(1, 1, figsize=(12, 6))
sns.lineplot(x=df.index, y=df['Temp'], ax=ax)
sns.scatterplot(x=outliers.index, y=outliers,
color='r', ax=ax)
plt.title('Tukey Method', fontsize='large');
Twitter - S-MAD
outliers = twitter(df['Temp'], period=12)
outliers.head()
Date
1981-01-15 25.0
1981-01-18 24.8
1981-02-09 25.0
1982-01-20 25.2
1982-02-15 26.3
Name: Temp, dtype: float64
fig, ax = plt.subplots(1, 1, figsize=(12, 6))
sns.lineplot(x=df.index, y=df['Temp'], ax=ax)
sns.scatterplot(x=outliers.index, y=outliers,
color='r', ax=ax)
plt.title('Tukey Method', fontsize='large');
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