Anomaly Detection in Time Series With Python

A stationary time series is a time series where the statistical properties, such as mean, variance, and autocorrelation, remain constant over time. In other words, it does not exhibit any significant trends, seasonality, or changes in statistical properties as the observations progress.

Scikit-learn is the most popular and easy-to-use library as it readily provides several machine learning models for use like K-Means, KNN, SVM, etc. However, there are others like statsmodels for ARIMA model implementation.

Hyperparameter tuning is the biggest challenge in the ARIMA model as it can affect its accuracy.

The purpose of anomaly detection is to identify outliers in data which can be important for business. They play an important role in stock markets, predictive maintenance of machine parts, and many more.

The choice of algorithm depends on the nature of the data and the type of anomalies you want to detect. Selecting an algorithm is entirely based on exploratory data analysis (EDA) and the accuracy achieved on test data.

Anomaly detection is used in factories for identifying the expected life of sensors installed in machines which help them know when a specific machine part needs to be replaced without affecting the manufacturing cycle.