Detecting Market Irregularities: Anomaly Detection in Financial Time-Series Data
Identifying market irregularities is crucial for making correct decisions. Anomalies in time-series data can indicate significant events, such as market crashes or booms, which can have a profound impact on investment strategies. This tutorial will guide you through the process of detecting anomalies in time-series data using Python, with a focus on financial data. We will leverage the power of object-oriented programming (OOP) to create a comprehensive and reusable solution.
Introduction
Anomaly detection in time-series data is a critical task in various domains, including finance, healthcare and manufacturing. In the financial sector, detecting anomalies can help identify unusual market behavior, such as sudden price spikes or drops, which may indicate potential opportunities or risks. This tutorial will focus on using Python to detect anomalies in financial time-series data, specifically stock prices.
We will use the yfinance library to download real financial data and implement an anomaly detection system using various techniques. By the end of this tutorial, you will have a solid understanding of how to detect anomalies in time-series data and how to apply these techniques to real-world financial data.
