A Practical Toolkit for Time Series Anomaly Detection, Using Python

A practical toolkit is presented for detecting various anomalies in time series data using Python. The guide outlines four specific types of anomalies: trend, volatility, single-point, and dataset-level. For each type, a theoretical explanation is provided, followed by Python code implementation for detection. The approach uses techniques like linear and polynomial regression to identify trends and includes a method for generating a synthetic dataset to test the detection methods.