Change detection

In statistical analysis, change detection or change point detection tries to identify times when the probability distribution of a stochastic process or time series changes. In general the problem concerns both detecting whether or not a change has occurred, or whether several changes might have occurred, and identifying the times of any such changes.

This article is about statistical time series analysis. For a focus on remote sensing and geographical change, see change detection (GIS). For detection of changes to web pages, see change detection and notification.
A plot of yearly volume of the Nile river at Aswan against time, an example of time series data commonly used in change detection
Yearly volume of the Nile river at Aswan, an example of time series data commonly used in change detection. Dotted line denotes a detected change point when a dam was built.[1]

Specific applications, like step detection and edge detection, may be concerned with changes in the mean, variance, correlation, or spectral density of the process. More generally change detection also includes the detection of anomalous behavior: anomaly detection.

Offline change point detection it is assumed that a sequence of length is available and the goal is to identify whether any change point(s) occurred in the series. This is an example of post hoc analysis and is often approached using hypothesis testing methods. By contrast, online change point detection is concerned with detecting change points in an incoming data stream.

Background

A time series measures the progression of one or more quantities over time. For instance, the figure above shows the level of water in the Nile river between 1870 and 1970. Change point detection is concerned with identifying whether, and if so when, the behavior of the series changes significantly. In the Nile river example, the volume of water changes significantly after a dam was built in the river. Importantly, anomalous observations that differ from the ongoing behavior of the time series are not generally considered change points as long as the series returns to its previous behavior afterwards.

Mathematically, we can describe a time series as an ordered sequence of observations . We can write the joint distribution of a subset of the time series as . If the goal is to determine whether a change point occurred at a time in a finite time series of length , then we really ask whether equals . This problem can be generalized to the case of more than one change point.

Algorithms

Online change detection

Using the sequential analysis ("online") approach, any change test must make a trade-off between these common metrics:

In a Bayes change-detection problem, a prior distribution is available for the change time.

Online change detection is also done using streaming algorithms.

Offline change detection

Basseville (1993, Section 2.6) discusses offline change-in-mean detection with hypothesis testing based on the works of Page[2] and Picard[3] and maximum-likelihood estimation of the change time, related to two-phase regression. Other approaches employ clustering based on maximum likelihood estimation,, use optimization to infer the number and times of changes,[4] via spectral analysis,[5] or singular spectrum analysis.[6]

Detection of changepoints in the Nile River flow data using a Bayesian method [7]

Statistically speaking, change detection is often considered as a model selection problem.[8][9][10] Models with more changepoints fit data better but with more parameters. The best trade-off can be found by optimizing a model selection criterion such as Akaike information criterion and Bayesian information criterion. Bayesian model selection has also been used. Bayesian methods often quantify uncertainties of all sorts and answer questions hard to tackle by classical methods, such as what is the probability of having a change at a given time and what is the probability of the data having a certain number of changepoints.[8]

"Offline" approaches cannot be used on streaming data because they need to compare to statistics of the complete time series, and cannot react to changes in real-time but often provide a more accurate estimation of the change time and magnitude.

Applications

Change detection tests are often used in manufacturing (quality control), intrusion detection, spam filtering, website tracking, and medical diagnostics.

Linguistic change detection

Linguistic change detection refers to the ability to detect word-level changes across multiple presentations of the same sentence. Researchers have found that the amount of semantic overlap (i.e., relatedness) between the changed word and the new word influences the ease with which such a detection is made (Sturt, Sanford, Stewart, & Dawydiak, 2004). Additional research has found that focussing one's attention to the word that will be changed during the initial reading of the original sentence can improve detection. This was shown using italicized text to focus attention, whereby the word that will be changing is italicized in the original sentence (Sanford, Sanford, Molle, & Emmott, 2006), as well as using clefting constructions such as "It was the tree that needed water." (Kennette, Wurm, & Van Havermaet, 2010). These change-detection phenomena appear to be robust, even occurring cross-linguistically when bilinguals read the original sentence in their native language and the changed sentence in their second language (Kennette, Wurm & Van Havermaet, 2010). Recently, researchers have detected word-level changes in semantics across time by computationally analyzing temporal corpora (for example:the word "gay" has acquired a new meaning over time) using change point detection.[11] This is also applicable to reading non-words such as music. Even though music is not a language, it is still written and people to comprehend its meaning which involves perception and attention, allowing change detection to be present.[12]

Visual change detection

Visual change detection is one's ability to detect differences between two or more images or scenes.[13] This is essential in many everyday tasks. One example is detecting changes on the road to drive safely and successfully. Change detection is crucial in operating motor vehicles to detect other vehicles, traffic control signals, pedestrians, and more.[14] Another example of utilizing visual change detection is facial recognition. When noticing one's appearance, change detection is vital, as faces are "dynamic" and can change in appearance due to different factors such as "lighting conditions, facial expressions, aging, and occlusion".[15] Change detection algorithms use various techniques, such as "feature tracking, alignment, and normalization," to capture and compare different facial features and patterns across individuals in order to correctly identify people.[15] Visual change detection involves the integration of "multiple sensors inputs, cognitive processes, and attentional mechanisms," often focusing on multiple stimuli at once.[16] The brain processes visual information from the eyes, compares it with previous knowledge stored in memory, and identifies differences between the two stimuli. This process occurs rapidly and unconsciously, allowing individuals to respond to changing environments and make necessary adjustments to their behavior.[17]

Cognitive change detection

There have been several studies conducted to analyze the cognitive functions of change detection. With cognitive change detection, researchers have found that most people overestimate their change detection, when in reality, they are more susceptible to change blindness than they think.[18] Cognitive change detection has many complexities based on external factors, and sensory pathways play a key role in determining one's success in detecting changes. One study proposes and proves that the multi-sensory pathway network, which consists of three sensory pathways, significantly increases the effectiveness of change detection.[19] Sensory pathway one fuses the stimuli together, sensory pathway two involves using the middle concatenation strategy to learn the changed behavior, and sensory pathway three involves using the middle difference strategy to learn the changed behavior.[19] With all three of these working together, change detection has a significantly increased success rate.[19] It was previously believed that the posterior parietal cortex (PPC) played a role in enhancing change detection due to its focus on "sensory and task-related activity".[20] However, studies have also disproven that the PPC is necessary for change detection; although these have high functional correlation with each other, the PPC's mechanistic involvement in change detection is insignificant.[20] Moreover, top-down processing plays an important role in change detection because it enables people to resort to background knowledge which then influences perception, which is also common in children. Researchers have conducted a longitudinal study surrounding children's development and the change detection throughout infancy to adulthood.[21] In this, it was found that change detection is stronger in young infants compared to older children, with top-down processing being a main contributor to this outcome.[21]

See also

References
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  2. Page, E. S. (June 1957). "On problems in which a change in a parameter occurs at an unknown point". Biometrika. 44 (1/2): 248–252. doi:10.1093/biomet/44.1-2.248. JSTOR 2333258.
  3. Picard, Dominique (1985). "Testing and estimating change-points in time series". Advances in Applied Probability. 17 (4): 841–867. doi:10.2307/1427090. JSTOR 1427090. S2CID 123026208.
  4. Yao, Yi-Ching (1988-02-01). "Estimating the number of change-points via Schwarz' criterion". Statistics & Probability Letters. 6 (3): 181–189. doi:10.1016/0167-7152(88)90118-6. ISSN 0167-7152.
  5. Ghaderpour, E.; Vujadinovic, T. (2020). "Change Detection within Remotely Sensed Satellite Image Time Series via Spectral Analysis". Remote Sensing. 12 (23): 4001. Bibcode:2020RemS...12.4001G. doi:10.3390/rs12234001.
  6. Alanqary, Arwa (2021). "Change Point Detection via Multivariate Singular Spectrum Analysis". Advances in Neural Information Processing Systems. 34: 23218–23230.
  7. Li, Yang; Zhao, Kaiguang; Hu, Tongxi; Zhang, Xuesong. "BEAST: A Bayesian Ensemble Algorithm for Change-Point Detection and Time Series Decomposition". GitHub.
  8. Zhao, Kaiguang; Wulder, Michael A; Hu, Tongx; Bright, Ryan; Wu, Qiusheng; Qin, Haiming; Li, Yang (2019). "Detecting change-point, trend, and seasonality in satellite time series data to track abrupt changes and nonlinear dynamics: A Bayesian ensemble algorithm". Remote Sensing of Environment. 232: 111181. Bibcode:2019RSEnv.232k1181Z. doi:10.1016/j.rse.2019.04.034. S2CID 201310998.
  9. Chen, Jie; Gupta, Arjun K (2001). "On change point detection and estimation". Communications in Statistics-simulation and Computation. 30 (3): 665–697. doi:10.1081/SAC-100105085. S2CID 121138768.
  10. Yoshiyuki, Ninomiya (2015). "Change-point model selection via AIC". Annals of the Institute of Statistical Mathematics (67): 943–961.
  11. Kulkarni Vivek; Rfou Rami; Perozzi Bryan; Skiena Steven (2015). "Statistically Significant Detection of Linguistic Change". Proceedings of the 24th International Conference on World Wide Web. pp. 625–635. arXiv:1411.3315. doi:10.1145/2736277.2741627. ISBN 9781450334693. S2CID 9298083.
  12. Kleinsmith, Abigail L. (2023). "Expertise effects on visual change detection in the music reading domain: Evidence from eye movements". In Dissertation Abstracts International: Section B: The Sciences and Engineering (Vol. 84, Issue 3–B).
  13. Ramey, Michelle M.; Henderson, John M.; Yonelinas, Andrew P. (December 2022). "Eye movements dissociate between perceiving, sensing, and unconscious change detection in scenes". Psychonomic Bulletin & Review. 29 (6): 2122–2132. doi:10.3758/s13423-022-02122-z. ISSN 1069-9384. PMID 35653039. S2CID 249276616.
  14. Morgenstern, Tina; Trommler, Daniel; Naujoks, Frederik; Karl, Ines; Krems, Josef F.; Keinath, Andreas (February 2023). "Comparing the sensitivity of the box task combined with the detection response task to the lane change test". Transportation Research Part F: Traffic Psychology and Behaviour. 93: 159–171. doi:10.1016/j.trf.2023.01.004. S2CID 256050914.
  15. Ventura, Paulo; Guerreiro, José Carlos; Pereira, Alexandre; Delgado, João; Rosário, Vivienne; Farinha-Fernandes, António; Domingues, Miguel; Cruz, Francisco; Faustino, Bruno; Wong, Alan C.-N. (April 2022). "Change detection vs. change localization for own-race and other-race faces". Attention, Perception, & Psychophysics. 84 (3): 627–637. doi:10.3758/s13414-022-02448-9. ISSN 1943-3921. PMID 35174465. S2CID 246904080.
  16. He, Chuanxiuyue; Rathbun, Zoe; Buonauro, Daniel; Meyerhoff, Hauke S.; Franconeri, Steven L.; Stieff, Mike; Hegarty, Mary (August 2022). "Symmetry and spatial ability enhance change detection in visuospatial structures". Memory & Cognition. 50 (6): 1186–1200. doi:10.3758/s13421-022-01332-z. ISSN 0090-502X. PMC 9365739. PMID 35705852.
  17. Williams, Jamal R.; Robinson, Maria M.; Schurgin, Mark W.; Wixted, John T.; Brady, Timothy F. (December 2022). "You cannot "count" how many items people remember in visual working memory: The importance of signal detection–based measures for understanding change detection performance". Journal of Experimental Psychology: Human Perception and Performance. 48 (12): 1390–1409. doi:10.1037/xhp0001055. ISSN 1939-1277. PMC 10257385. PMID 36222675.
  18. Barnas, Adam J.; Ward, Emily J. (October 2022). "Metacognitive judgements of change detection predict change blindness". Cognition. 227: 105208. doi:10.1016/j.cognition.2022.105208. PMID 35792349. S2CID 239626887.
  19. Liu, Kang; Li, Xuelong (July 2022). "Bio-inspired Multi-Sensory Pathway Network for Change Detection". Cognitive Computation. 14 (4): 1421–1434. doi:10.1007/s12559-021-09968-w. ISSN 1866-9956. S2CID 247283289.
  20. Oude Lohuis, Matthijs N.; Marchesi, Pietro; Pennartz, Cyriel M.A.; Olcese, Umberto (2022-06-29). "Functional (ir)Relevance of Posterior Parietal Cortex during Audiovisual Change Detection". The Journal of Neuroscience. 42 (26): 5229–5245. doi:10.1523/JNEUROSCI.2150-21.2022. ISSN 0270-6474. PMC 9236290. PMID 35641187.
  21. Deguire, Florence; López-Arango, Gabriela; Knoth, Inga Sophia; Côté, Valérie; Agbogba, Kristian; Lippé, Sarah (2022-11-21). "Developmental course of the repetition effect and change detection responses from infancy through childhood: a longitudinal study". Cerebral Cortex. 32 (23): 5467–5477. doi:10.1093/cercor/bhac027. ISSN 1047-3211. PMC 9712715. PMID 35149872.

Further reading
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