Time Series Clustering for Anomaly Detection: Implementing K-means and Hierarchical Clustering to Detect Abnormal Market Behavior

In this tutorial, we will explore the concept of time series clustering for anomaly detection. Time series data is a sequence of observations collected over time and clustering is a technique used to group similar data points together. By applying clustering algorithms to time series data, we can identify patterns and detect anomalies in various domains, such as finance, healthcare and manufacturing.

We will focus on two popular clustering algorithms: K-means and hierarchical clustering. K-means is a centroid-based algorithm that partitions data into K clusters, while hierarchical clustering creates a hierarchy of clusters using a bottom-up or top-down approach. We will implement these algorithms using Python and apply them to financial market data to detect abnormal behavior.

Table of Contents

1. Getting Started
2. Data Collection
3. Data Preprocessing
4. K-means Clustering
5. Hierarchical Clustering
6. Anomaly Detection
7. Conclusion

Getting Started

Before we dive into the implementation, let’s make sure we have all the necessary libraries installed. We will be using the following libraries:

• numpy: for numerical operations
• pandas: for data manipulation
• matplotlib: for data visualization
• scikit-learn: for clustering algorithms

You can install these libraries using the following command:

pip install numpy pandas matplotlib scikit-learn

Now that we have the required libraries, let’s move on to data collection.

Data Collection

To demonstrate time series clustering for anomaly detection, we will use financial market data. We will download historical stock price data for a few selected securities from Yahoo Finance using the yfinance library. This library allows us to easily fetch financial data for various assets, such as…