[Guide] Anomaly detection algorithm

#Download necessary libraries
!pip install pandas
!pip install matplotlib
!pip install prophet

#Load necessary libraries
from prophet import Prophet

# Initialize the model and set its sensitivity
model = Prophet(interval_width=0.95)

# Fit the model
model.fit(data)

# Forecast on the original data to get the bounds
forecast = model.predict(data)

#Calculate the anomalies plus the upper and lower bounds.
anomalies = data.loc[(data['y'] > forecast['yhat_upper']) | (data['y'] < forecast['yhat_lower'])]

#Visualize the results
import matplotlib.pyplot as plt

# Plot the Prophet forecast
fig1 = model.plot(forecast)

# Overlay the anomalies
plt.scatter(anomalies['ds'], anomalies['y'], color='red', s=50, label='Anomalies')
plt.legend()
plt.show()

#The red dotes are dates that are considered as anomalies.

#Print the data points that were flagged as anomalies
print(anomalies[['ds', 'y']])

							ds      y
1806  2022-02-22  1.621
1807  2022-02-23  1.623
1808  2022-02-24  1.626
1809  2022-02-25  1.635
1810  2022-02-26  1.640
...          ...    ...
1932  2022-06-28  2.123
1933  2022-06-29  2.120
1934  2022-06-30  2.112
1935  2022-07-01  2.102
1936  2022-07-02  2.094