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Mastering Artificial Intelligence with Azure Cognitive Services

Univariate Anomaly Detection - Change Points and Best Practices

Explore how to detect change points in time-series data with Azure Decision Cognitive Services. Understand the code, practical application, and best practices for univariate anomaly detection, including data preparation, handling missing data, and when to apply full dataset vs. last-point detection for optimized AI monitoring.

We'll cover the following...

To recap, in the previous lesson we discussed the two approaches to detect the anomalies in time-series data. In this lesson, we’ll explore the third scenario—detecting change points in the time-series data. We’ll take the same sample data set of the stock prices with their timestamp.

Detecting change points in the time-series data

Let’s explore how change points are detected in the time-series data.

C++
from azure.ai.anomalydetector import AnomalyDetectorClient
from azure.ai.anomalydetector.models import DetectRequest, TimeSeriesPoint, TimeGranularity
from azure.core.credentials import AzureKeyCredential
import pandas as pd
anomaly_detector_client = AnomalyDetectorClient(
            AzureKeyCredential(anomaly_detector_key), 
            anomaly_detector_endpoint
        )
series_data = []
data_file = pd.read_csv("univariate_data.csv", header = None, parse_dates = [0])
for index, row in data_file.iterrows():
    series_data.append(TimeSeriesPoint(timestamp=row[0], value=row[1]))
request = DetectRequest(series = series_data, granularity = TimeGranularity.daily, sensitivity = 97)
try:
    response = anomaly_detector_client.detect_change_point(request)
except Exception as e:
    print(e)
change_point_index = []
for index, change_point_found in enumerate(response.is_change_point):
    if change_point_found:
        change_point_index.append(index)
if len(change_point_index) > 0:
    for i in range(len(change_point_index)):
        print("Change point at index: ", change_point_index[i])
        print("Stock Price before: $", data_file.iloc[i - 1][1])
        print("Original Stock Price: $", data_file.iloc[i][1])
        print("Stock Price after: $", data_file.iloc[i + 1][1])
        print()
else:
    print('No anomalies were detected in the time series.')

Explanation:

  • The code is almost the same as the code we discussed in the
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