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Introduction to Change Point

This article gives a basic information regarding the change points that occur in excel and in other files. We propose the detection methods for these change points and they are analyzed with a real time example. The features and applications of the change point are also discussed later.

Definition of Change Point

In the statistical studies, the Change Point Detection is known as a Stochastic Process, which is used to identify the timely changes according to changes in any one of these parameters- when the probability distribution of the system changes or when the time series of the system changes. It deals with the problems that are relating to determining or with detecting whether the time change has occurred or not and if occurred, it determines the time limit during which the change has occurred. The change detection is sometimes also referred to as the anomaly detection, as it deals with the different detection techniques like step and edge value detection, that are connected to or in coherence with the changes which occur in the values like mean, median, variance and covariance.

The analysis of online changes is one of the most widely used technique in present day, which is carried out using the sequential or steep by step analysis and hence is referred to as streaming algorithm. In Online Detection, it is used to measure the change that is made using the relation and association between the metrics like detection delay, false alarm rate and misdetection rate. There are various types of change detection techniques like:

Minimax Change Detection

As the name suggests, the objective of this Minimax change detection technique is to reduce the delay that is expected to take place in a system. In some worst-cases that can occur during the time distribution, this Detection Technique is carried out by CUSUM procedure which is one of the most popular techniques.

Offline Change Detection

This detection method was found out by Basseville. It observes the change in mean detection of the system. This estimation is related to the EM algorithm method and other related methods like two-phase regression, clustering and in the maximum likelihood estimation of the system variables.

Linguistic Change Detection

This type of detection method deals with the ability to detect the word-level changes or changes in language that occur in the presentations of the same sentence.

Change Point Detection Packages

Many R community packages have been developed for the change point detection. They already exist in the CRAN and focus effectively on the change point detection. Let us discuss some of the popular change point software:

CPM:

The CPM method is used in order to identify or detect the changes in the parametric and non-parametric sequences of the given system. It is more helpful in the detection of multiple point change that occurs in the time series from the unknown distribution. This method can be applied for the data streams where only one observation can be made. A special case of CPM method requires that the detection points should be displayed. For each detection process, we store the values of the corresponding number of logins.

This type of detection process makes use of two types of parameters, where one parameter is related to the testing of statistic value and the second parameter is the number of observations that are made at the beginning of the process and until the change occurred in the points. The test statistic value offers multiple versions to detect the changes depending on the type of distribution. They have the ability to quantify or measure the delay but unfortunately, this CPM is no longer used in the CRAN process.

BCP:

This package is used for performing the Bayesian analysis of change points in problems. This is an R package that was designed using the Markov chain Carlo to find the multiple changes in point that occurs within the sequential analysis. This package is restricted to the implementation of multivariate case.

The BCP approach uses three types of parameters. One of the parameter is the probability threshold of the estimated probabilities.

ECP:

This package is specially designed for the analysis of non-parametric multiple point change in the multivariate data. The ECP package is similar to the hierarchical or sequential process used in the EM Algorithm and offers the top-down and bottom-up approach for the change point detection process. Usually, the top-down approach is recommended for the Tableau where minimum number of observations is required.

The process involved in the change point detection is:

• First, when we perform the analysis, the analyst can make use of the background knowledge about the data and the possible effects from the external sources affecting the data. This kind of observation is not easily gathered for the algorithm.
• Second, this is the process that takes place before the final step. This process mainly focuses on the less complex decision making technique.
• The third and the final process involves the submission of the visual feedback that demonstrates how these algorithms perform and give the results by providing a second opinion.
• 

• Fig. 1- The Dashboard Representation

  The above dashboard represents a very simple structure that shows the trial- and- error and experimental observations, rather than theoretical observations that are made using the packages discussed above. There are  various options like signature, and the parameters are held on the right side of the dashboard that allow to interact with the algorithm and in understanding the data and in the filtering process. The following advancement or progress can be followed in the working of dashboard:

  • Loading of packages and initialization of various parameters
  • Triggering the change point detection
  • Extraction of exact location of the change points by applying the filtering process
  • Calculating the segment value of the mean value identified in the change point.

  Change Point Analysis

  Change-point analysis is one such tool that is used for determining whether the change has taken place or not. It is also capable of finding the changes that have been missed while estimating the control chart. Change-point analysis has the ability to study how a process changes over time as it is an effective way in determining the historical data and in dealing with the large amount of data. It provides the control over the overall error rate and is more flexible and a simpler method to be implemented.

  Multiple changes can be found by the change point analysis and detailed information is extracted that can be used for the future purpose. This analysis can be performed for all types of time ordered data such as attributed data, abnormal distributions and discrete or distinct data which does not fall in the required set of data. The change point control is similar to the traditional control chart method and the major difference between the change point analysis and control charting is that the control charts are to be updated for each and every collection of point, while the change point analysis is performed for the data that is collected for the first time.

  Control charts are better at detecting the abnormal points more quickly while change point analysis is used to detect the changes that are missed in the control charts. This method is applicable for the system with thousands of data points along with the numerous points.  Let us consider the US trade deficits during 1987-1988 as the example for the change point analysis.

  	Jan	Feb	Mar	Apr	May	Jun	Jul	Aug	Sep	Oct	Nov	Dec
  1987	10.7	13.0	11.4	11.5	12.5	14.1	14.8	14.1	12.6	16.0	11.7	10.6
  1988	10.0	11.4	7.9	9.5	8.0	11.8	10.5	11.2	9.2	10.1	10.4	10.5

  

                             Fig. 2- Plot for the US deficit data

  
  

  The trade deficit plot shows to be in lower rate in 1988 than in 1987. There are various approaches followed for performing the analysis.  Both control chart and change point model were applied for this process.  However, the control chart detected the change barely. But, the change point analysis provided additional information other than the control chart. The procedure suggested by Taylor is used along with combination of cumulative sum chart and with bootstrapping or resampling method for the detection of the changes. Practice is required for the implementation of the CUSUM procedure.

  
  

  

                                   Fig.3- Change point analysis

  For the change point analysis, excel implementation purpose, the excel add-in software is used and the change point analyzer is used for this purpose.

  Advantages of Change Point

  Some of the features of change point analysis are as follows:

  • This analysis is more powerful in detecting the small as well as changes that are sustained or maintained over a long period of time.
  • It reduces the possibility of false or erroneous detections by implementing the control of change in error rate while, control charts use point wise error rate for large data that produces more false detections.
  • It provides a better approach towards the abnormal data.
  • This type of analysis is more flexible. The analysis is based on the single assumption method only.
  • The method is simpler and easy to use and to be interpreted. It has the ability to automate the difficult process. 

  
  Applications

  The change detection test is useful and better suited in the manufacturing of equipment that aid in the quality control and in the detection of intrusion, filtering of spam, tracking of websites and in the diagnosis of medical aids. The change point detection is more helpful in the field of simulation process and in designing the filters for the digital signal processing.

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