5 Changepoint Detection algorithms every Data Scientist should know | by Satyam Kumar | Mar, 2023

Essential guide to changepoint detection algorithms for time series analysis

Time-series analytics is one of the topics a data scientist must have exposure to. Time-series analysis comprises the process and mathematical set of tools used for looking into time-series data to learn what happened, when and why it happened, and what is most likely to occur in the future.

Change points are sudden variations in time series data that may represent transitions occurring between states. While working with time-series forecasting use-case, it’s essential to detect the changepoints to identify when the probability distribution of a stochastic process or time series changes.

This article will discuss and implement 4 such changepoint detection techniques and benchmark their performance.

1. Piece-wise Linear Regression:

When a changepoint occurs, the pattern or trend of the time-series data changes. The basic idea of the piece-wise linear regression model is to identify such changes in patterns or trends over different data regions. In the case of the presence of a changepoint, the values of the coefficients are comparatively higher or lower than the nearby regions.

Pseudo-code of the Implementation:
1. Divide the time-series data into sub-sections of x (say 100) days
2. Iterate through each sub-section of the data:
- Train data: enumerate of the data
- Target data: raw time-series value
- Train a linear regression model on train and target data
- compute coeffcient of the trained LR model
3. Plot the coefficients

The red line in the above-mentioned image represents the coefficient value for each linear regression model trained on that subset or section of time-series data. Coefficients are the value that multiplies by the predicted values, so the higher the predictions, the higher the coefficients will be, and vice-versa.

2. Change Finder:

Change finder is an open-source Python package that offers real-time or online change point detection algorithms. It uses SDAR (Sequentially Discounting AutoRegressive) learning algorithm that expects that the AR processes before and after the change point will be different.

The SDAR method has two learning phases:

• First Learning Phase: Produces an intermediate score called the anomaly score
• Second Learning Phase: Produces the change-point score that can detect a change point

3. Ruptures:

Ruptures is an open-sourced Python library that offers algorithms for offline change-point detection. This package detects change points by analyzing the entire sequence and segmenting non-stationary signals.

Ruptures offer 6 algorithms or techniques to detect changepoints in the time series data:

• Dynamic Programming
• PELT (Pruned Exact Linear Time)
• Kernel Change Detection
• Binary Segmentation
• Bottom-up segmentation
• Window sliding segmentation

In this article, we have discussed 3 popular hands-on techniques to identify change points in time-series data. The change-point detection algorithms have various applications including medical condition monitoring, human activity analysis, website tracking, etc.

Apart from the above-discussed change-point detection algorithms, there are other supervised and unsupervised CPD algorithms.

1. Change finder Documentation: https://pypi.org/project/changefinder/
2. Ruptures Documentation: https://centre-borelli.github.io/ruptures-docs/

Thank You for Reading

Essential guide to changepoint detection algorithms for time series analysis

Time-series analytics is one of the topics a data scientist must have exposure to. Time-series analysis comprises the process and mathematical set of tools used for looking into time-series data to learn what happened, when and why it happened, and what is most likely to occur in the future.

Change points are sudden variations in time series data that may represent transitions occurring between states. While working with time-series forecasting use-case, it’s essential to detect the changepoints to identify when the probability distribution of a stochastic process or time series changes.

This article will discuss and implement 4 such changepoint detection techniques and benchmark their performance.

1. Piece-wise Linear Regression:

When a changepoint occurs, the pattern or trend of the time-series data changes. The basic idea of the piece-wise linear regression model is to identify such changes in patterns or trends over different data regions. In the case of the presence of a changepoint, the values of the coefficients are comparatively higher or lower than the nearby regions.

Pseudo-code of the Implementation:
1. Divide the time-series data into sub-sections of x (say 100) days
2. Iterate through each sub-section of the data:
- Train data: enumerate of the data
- Target data: raw time-series value
- Train a linear regression model on train and target data
- compute coeffcient of the trained LR model
3. Plot the coefficients

The red line in the above-mentioned image represents the coefficient value for each linear regression model trained on that subset or section of time-series data. Coefficients are the value that multiplies by the predicted values, so the higher the predictions, the higher the coefficients will be, and vice-versa.

2. Change Finder:

Change finder is an open-source Python package that offers real-time or online change point detection algorithms. It uses SDAR (Sequentially Discounting AutoRegressive) learning algorithm that expects that the AR processes before and after the change point will be different.

The SDAR method has two learning phases:

• First Learning Phase: Produces an intermediate score called the anomaly score
• Second Learning Phase: Produces the change-point score that can detect a change point

3. Ruptures:

Ruptures is an open-sourced Python library that offers algorithms for offline change-point detection. This package detects change points by analyzing the entire sequence and segmenting non-stationary signals.

Ruptures offer 6 algorithms or techniques to detect changepoints in the time series data:

• Dynamic Programming
• PELT (Pruned Exact Linear Time)
• Kernel Change Detection
• Binary Segmentation
• Bottom-up segmentation
• Window sliding segmentation

In this article, we have discussed 3 popular hands-on techniques to identify change points in time-series data. The change-point detection algorithms have various applications including medical condition monitoring, human activity analysis, website tracking, etc.

Apart from the above-discussed change-point detection algorithms, there are other supervised and unsupervised CPD algorithms.

1. Change finder Documentation: https://pypi.org/project/changefinder/
2. Ruptures Documentation: https://centre-borelli.github.io/ruptures-docs/

Thank You for Reading