Anomaly detection algorithm

Date

September 2, 2023

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Anomaly detection is a data-driven concept that is widely used by businesses across industries in order to identify potential anomalies in a performance of a product. The concept applies in multiple situations, from fraud detection, to performance monitoring, budget optimization etc.

For this demonstration, we'll be using a simple dataset and the Prophet algorithm. Its not a pure anomaly detection algorithm but it can serve this purpose too. We'll work with a dataset detailing prices for various fuel types. You can download it from this website (from the 1st table).

We'll be using a Collab notebook, so there's no need to download any additional tools to run the provided code.

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

#Load necessary libraries
from prophet import Prophet

# Loading the dataset into a pandas DataFrame
import pandas as pd

#Load the dataset
#save and upload the csv to the collab notebook. Then copy the path of csv and paste it here.
df = pd.read_csv('/content/your_saved_file.csv')

# Select and rename the relevant columns for Prophet
data = df[['index', 'diesel']].rename(columns={'index': 'ds', 'diesel': 'y'})

# Display the first few rows of the transformed dataset
data.head()

# 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

Run the code on your own, adjust the interval_width and check the different results that will be generated. Also, try to expand the capabilities of Prophet or try other algorithms (like Luminaire) to get a better understanding of how anomaly detection works.

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