Anomaly detection is the process of identifying outliers or unusual patterns in data

Anomalies can be caused by errors, rare events, cyberattacks, equipment malfunctions, or other unusual occurrences.

Anomaly detection can be performed on different types of data, including numerical data, categorical data, time series data,etc.

There are various types of anomaly detection like:Statistical-based,Machine learning-based anomaly detection,Supervised and unSupervised detection ,etc.

Common techniques used in time series anomaly detection include moving averages, ARIMA models, exponential smoothing, and outlier detection algorithms.

Anomaly detection algorithms can be evaluated using metrics such as precision, recall, F1-score

Preprocessing and feature engineering play a crucial role in anomaly detection

Anomaly detection is an ongoing process, as the definition of what constitutes an anomaly may change over time.