Developing Machine Learning Models for Anomaly Detection in Time Series Data

This tutorial aims to provide a comprehensive guide to developing machine learning models for anomaly detection in time series data, with a focus on financial market data. We will explore various techniques and algorithms that can be used to detect anomalies in time series data and we will implement these techniques using Python.

Photo by Jess Bailey on Unsplash

Anomaly detection is a critical task in many domains, including finance, cybersecurity and industrial monitoring. In the financial market, detecting anomalies can help identify fraudulent activities, predict market crashes and improve risk management strategies. Time series data, which consists of a sequence of data points collected over time, is commonly encountered in financial market analysis. By analyzing the patterns and trends in time series data, we can identify abnormal behavior that deviates from the expected patterns.

In this tutorial, we will cover the following topics:

1. Introduction to Anomaly Detection
2. Preparing the Data
3. Exploratory Data Analysis
4. Feature Engineering
5. Building Machine Learning Models
6. Evaluating Model Performance
7. Deploying the Model