{ "id": "ep256yx7ty", "slug": "step-detection", "title": "Step detection", "updated": "2026-10-15", "topic_path": [ { "id": "physical", "label": "Physical world and mathematics", "api_url": "https://www.edgepedia.org/api/v1/topics/physical" }, { "id": "physical.math", "label": "Mathematics and statistics", "api_url": "https://www.edgepedia.org/api/v1/topics/physical.math" }, { "id": "physical.math.statistics", "label": "Statistics and probability", "api_url": "https://www.edgepedia.org/api/v1/topics/physical.math.statistics" }, { "id": "physical.math.statistics.stats_statistical_inference", "label": "Statistical inference, estimation, sampling, and testing", "api_url": "https://www.edgepedia.org/api/v1/topics/physical.math.statistics.stats_statistical_inference" } ], "geo": [ { "id": "geo.nongeo.t1946.physical.math.statistics.stats_statistical_inference", "label": "Non-geographic · 1946 to 2000: Statistical inference, estimation, sampling, and testing", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t1946.physical.math.statistics.stats_statistical_inference", "path": [ { "id": "geo.nongeo", "label": "Non-geographic", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo" }, { "id": "geo.nongeo.t1946", "label": "Non-geographic · 1946 to 2000", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t1946" }, { "id": "geo.nongeo.t1946.physical", "label": "Physical world and mathematics", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t1946.physical" }, { "id": "geo.nongeo.t1946.physical.math", "label": "Mathematics and statistics", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t1946.physical.math" }, { "id": "geo.nongeo.t1946.physical.math.statistics", "label": "Statistics and probability", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t1946.physical.math.statistics" }, { "id": "geo.nongeo.t1946.physical.math.statistics.stats_statistical_inference", "label": "Statistical inference, estimation, sampling, and testing", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t1946.physical.math.statistics.stats_statistical_inference" } ] } ], "excerpt": "Step detection is a signal processing and statistical method that locates abrupt steps in a noisy time series, estimating when the signal jumps and each segment's level.", "snippet": "Step detection is a signal processing and statistical method that locates abrupt steps in a noisy time series, estimating when the signal jumps and each segment's level.", "node": "physical.math.statistics.stats_statistical_inference", "markdown": "# Step detection\n\nStep detection is a signal processing and statistical method that locates abrupt changes, or steps, in the level of a noisy time series, estimating both the times at which the signal jumps and the constant level of each segment between jumps. It is used wherever a system is expected to sit in discrete states, from molecular motors to quality-control monitoring. Methods are divided into offline (retrospective) algorithms that analyze a complete record and online (sequential) algorithms that process each data point as it arrives, with \\( \\varepsilon \\)-real-time algorithms needing at least \\( \\varepsilon \\) new samples before declaring a change.[1](https://ar5iv.labs.arxiv.org/html/1801.00718) • [2](https://eecs.wsu.edu/~cook/pubs/kais16.2.pdf)\n\n| Key fact | Detail |\n|---|---|\n| Output | Change-point times and the piecewise-constant segment levels between them[1](https://ar5iv.labs.arxiv.org/html/1801.00718) |\n| Standard model | \\( y_t = f_t + \\varepsilon_t \\) with \\( f_t \\) piecewise constant and \\( \\varepsilon_t \\sim N(0, \\sigma^2) \\) independent[3](https://www.lancaster.ac.uk/~romano/teaching/2425MATH337/MATH337--Changepoint-Detection.pdf) |\n| Earliest methods | Industrial quality control in the 1920s; the CUSUM procedure of E. S. Page (1954)[4](https://par.nsf.gov/servlets/purl/10492374) • [5](https://doi.org/10.1093/biomet/41.1-2.100) |\n| Default penalty | \\( \\beta = 2 \\log n \\) for Gaussian, independent noise with well-estimated variance[6](https://www.jstatsoft.org/article/download/v109i07/4577) |\n| Detectability limit | Good step recovery needs more than about \\( 1/\\mathrm{SNR}^2 \\) samples between steps[7](https://pmc.ncbi.nlm.nih.gov/articles/PMC3743561/) |\n| Main failure mode | Over-estimation of the number of steps under autocorrelated or heavy-tailed noise[8](https://onlinelibrary.wiley.com/doi/10.1002/sta4.291) |\n\n## How it works\n\nThe underlying model is a signal-plus-noise model \\( y_t = f_t + \\varepsilon_t \\), where \\( f_t \\) is piecewise constant and the noise is independent and Gaussian with variance \\( \\sigma^2 \\); a step is a change in the mean \\( f_t \\) at a changepoint.[3](https://www.lancaster.ac.uk/~romano/teaching/2425MATH337/MATH337--Changepoint-Detection.pdf) Offline detection is cast as a model selection problem: choose the segmentation that minimizes a quantitative cost, adding a complexity penalty when the number of changes is unknown.[1](https://ar5iv.labs.arxiv.org/html/1801.00718) An offline algorithm is characterized by three elements: a cost function, a search method, and a constraint on the number of changes.[1](https://ar5iv.labs.arxiv.org/html/1801.00718)\n\nThe CUSUM statistic compares the mean of the data to the left and right of each candidate changepoint; for a single change, minimizing the Gaussian segmentation cost over all locations is equivalent to maximizing the CUSUM statistic, and CUSUM can be viewed as a special case of a likelihood ratio test comparing no-change against single-change models.[3](https://www.lancaster.ac.uk/~romano/teaching/2425MATH337/MATH337--Changepoint-Detection.pdf) In its sequential form it accumulates deviations from a target and signals a change when the cumulative sum exceeds a threshold.[2](https://eecs.wsu.edu/~cook/pubs/kais16.2.pdf)\n\nOnline performance is measured by the average run length (ARL, the expected stopping time under the pre-change distribution) and the expected detection delay (EDD) after a change.[4](https://par.nsf.gov/servlets/purl/10492374) A CUSUM-based procedure with false alarm probability at most \\( a \\) detects a mean change with delay of order \\( \\sigma^2 \\kappa^{-2} \\log(\\Delta/a) \\), which is minimax optimal, where \\( \\kappa \\) is the change size, \\( \\Delta \\) the spacing, and \\( \\sigma \\) the noise level.[9](https://wrap.warwick.ac.uk/id/eprint/189987/1/A%20note%20on%20online%20change%20point%20detection.pdf) Online detection becomes impossible when the signal-to-noise ratio \\( \\kappa^2 \\Delta / \\sigma^2 \\) falls below \\( \\log(\\Delta/a) \\), a phase transition nearly identical to the offline setting.[9](https://wrap.warwick.ac.uk/id/eprint/189987/1/A%20note%20on%20online%20change%20point%20detection.pdf)\n\n## How it is done\n\nA practitioner first fixes the noise model. If the noise is approximately Gaussian and independent and the variance estimate is good, the default penalty \\( \\beta = 2 \\log n \\) is appropriate; larger penalties compensate for positively autocorrelated noise, and the CROPS procedure computes optimal segmentations across a range of penalties so the analyst can inspect sensitivity.[6](https://www.jstatsoft.org/article/download/v109i07/4577) [Inflation](https://www.edgepedia.org/inflation) of the penalty is a surprisingly effective patch for moderate autocorrelation, but fails once the autocorrelation coefficient exceeds about 0.5, where models that explicitly represent the correlation become necessary.[10](https://ar5iv.labs.arxiv.org/html/2005.01379)\n\nMinimum segment length is the second key setting: it improves robustness to outliers and heavy-tailed noise, but can invalidate PELT-style pruning (making results slightly sub-optimal) and causes true changes closer than the minimum to be missed.[6](https://www.jstatsoft.org/article/download/v109i07/4577) A minimum segment length of 4 noticeably improves penalized squared-error performance, and methods with an implicit minimum segment length, such as IDetect and MOSUM, perform strongly under autocorrelated or heavy-tailed noise.[8](https://onlinelibrary.wiley.com/doi/10.1002/sta4.291)\n\nDomain-specific analogs exist: a step finder for single-molecule data uses penalty weight \\( W = 9\\sigma^2 \\) to balance under- and over-fitting,[7](https://pmc.ncbi.nlm.nih.gov/articles/PMC3743561/) an \\( \\ell_1 \\)-regularized method bounds its regularization parameter by \\( 2\\sigma < \\gamma < h \\cdot w/2 \\) for step height \\( h \\) and width \\( w \\),[11](https://pmc.ncbi.nlm.nih.gov/articles/PMC3136774/) and in electrochemical nano-impact data the height threshold is the crucial parameter, since steps are only defined relative to signal size.[12](https://pubs.rsc.org/en/content/articlehtml/2025/fd/d4fd00130c)\n\n## Origin\n\nStatistical monitoring of sequences began with industrial quality control in the 1920s, motivated by manufacturing process monitoring.[4](https://par.nsf.gov/servlets/purl/10492374) [Abraham Wald](https://www.edgepedia.org/abraham-wald)'s 1946 work on sequential analysis provided the mathematical prelude to online change-point detection,[13](https://doi.org/10.1214/aoms/1177730889) and E. S. Page introduced the CUSUM continuous inspection scheme in [Biometrika](https://www.edgepedia.org/biometrika) in 1954, based on the log-likelihood ratio between the pre-change and post-change distributions.[5](https://doi.org/10.1093/biomet/41.1-2.100) • [4](https://par.nsf.gov/servlets/purl/10492374)\n\nOptimal multiple segmentation arrived with dynamic programming: B. Jackson and colleagues gave an algorithm for optimal partitioning of data on an interval in IEEE Signal Processing Letters in 2005,[14](https://doi.org/10.1109/lsp.2001.838216) F. Friedrich and colleagues showed in 2008, in the Journal of Computational and Graphical Statistics, that the Potts functional can be computed by dynamic programming at \\( O(n^2) \\) cost,[15](https://doi.org/10.1198/106186008x285591) and R. Killick, P. Fearnhead, and I. A. Eckley introduced PELT in 2012 in the [Journal of the American Statistical Association](https://www.edgepedia.org/journal-of-the-american-statistical-association) with exactness and, under mild conditions, linear cost.[16](https://doi.org/10.1080/01621459.2012.737745) Stepwise jump placement is a greedy knot-insertion scheme.[17](https://doi.org/10.1038/nature04928) • [18](https://royalsocietypublishing.org/doi/10.1098/rspa.2010.0671)\n\n## Variants\n\n**Filtering methods** run online with small state. CUSUM achieves constant memory and computational cost and is asymptotically optimal when the pre- and post-change distributions are precisely known;[4](https://par.nsf.gov/servlets/purl/10492374) adaptive CUSUM variants insert an adaptive estimate of the new mean to handle unknown post-change distributions.[4](https://par.nsf.gov/servlets/purl/10492374)\n\n**Optimal segmentation** solves the penalized problem exactly. [Dynamic programming](https://www.edgepedia.org/dynamic-programming) costs at least quadratically in series length;[19](https://link.springer.com/article/10.1007/s11222-016-9636-3) PELT uses inequality-based pruning for exactness with expected linear cost,[16](https://doi.org/10.1080/01621459.2012.737745) • [19](https://link.springer.com/article/10.1007/s11222-016-9636-3) and FPOP, from Robert Maidstone and colleagues in 2016, always prunes more than PELT and is efficient regardless of the number of changepoints.[19](https://link.springer.com/article/10.1007/s11222-016-9636-3) An \\( \\ell_1 \\)-penalized least-squares reformulation turns offline estimation into variable selection over dummy variables for all possible change locations.[20](https://papers.nips.cc/paper_files/paper/2007/file/e5841df2166dd424a57127423d276bbe-Paper.pdf)\n\n**Approximate search** trades exactness for speed. Binary segmentation adds changepoints one at a time, is roughly linear in data size, and can poorly estimate the number and position of changes;[19](https://link.springer.com/article/10.1007/s11222-016-9636-3) wild binary segmentation, proposed by Piotr Fryzlewicz in 2014, performs multiple CUSUM tests over randomly chosen sub-intervals with a better localization rate;[21](https://doi.org/10.1214/14-aos1245) circular binary segmentation, from A. B. Olshen and colleagues in 2004, detects pairs of break points in array DNA copy number data;[22](https://doi.org/10.1093/biostatistics/kxh008) seeded binary segmentation (S. Kovács and colleagues, 2022) is a general fast methodology with optimality properties;[23](https://doi.org/10.1093/biomet/asac052) and the MOSUM procedure of Birte Eichinger and Claudia Kirch (2017) estimates multiple changes from moving sums.[24](https://doi.org/10.3150/16-bej887)\n\n**Bayesian approaches** include the product partition models of Daniel Barry and J. A. Hartigan (1993),[25](https://doi.org/10.1080/01621459.1993.10594323) Bayesian online changepoint detection (Ryan Prescott Adams and [David J. C. MacKay](https://www.edgepedia.org/david-j-c-mackay), 2007), which tracks the posterior distribution of a run-length variable that resets to 0 at a change,[26](https://doi.org/10.48550/arxiv.0710.3742) • [2](https://eecs.wsu.edu/~cook/pubs/kais16.2.pdf) and hidden Markov models, applied to single-molecule FRET trajectories by Sean A. McKinney, [Chirlmin Joo](https://www.edgepedia.org/chirlmin-joo), and [Taekjip Ha](https://www.edgepedia.org/taekjip-ha) in 2006.[27](https://doi.org/10.1529/biophysj.106.082487) BNP-Step is a Bayesian nonparametric step finder that determines the number and location of transitions without assuming geometric holding times, using a beta-Bernoulli process prior to activate candidate steps and propagating measurement uncertainty into posterior uncertainty over step locations, numbers, and levels.[28](https://doi.org/10.1016/j.bpj.2024.01.008)\n\n**Biophysics step finders** form their own lineage: the \\( \\chi^{2} \\) stepwise method of Kerssemakers and colleagues iteratively places the most prominent step and needs only two parameters (expected number of steps and filter rank),[29](https://pmc.ncbi.nlm.nih.gov/articles/PMC2134886/) AutoStepfinder is a fast automated iterative-partition method with a dual-pass strategy for widely varying step sizes,[30](https://ceesdekkerlab.nl/wp-content/uploads/2019/08/1-s2.0-S2666389921000829-main.pdf) the Steps and Bumps method uses \\( \\ell_1 \\)-regularized piecewise-constant smoothing with a physical model of correlated noise and smooth transitions,[11](https://pmc.ncbi.nlm.nih.gov/articles/PMC3136774/) and a 2015 nonparametric method analyzes the probability distribution of data values, assuming neither Gaussian nor colored noise nor Heaviside step forms.[31](https://onlinelibrary.wiley.com/doi/10.1002/cyto.a.22631)\n\n## Applications\n\nSingle-molecule biophysics is the heaviest user: applied to kinesin motor data, step-detection methods found strong evidence for sub-8-nm steps of the cargo center of mass under forces below 5 pN, showing that multiple motors are not forced into lock step.[29](https://pmc.ncbi.nlm.nih.gov/articles/PMC2134886/) BNP-Step was demonstrated on force spectroscopy experiments,[28](https://doi.org/10.1016/j.bpj.2024.01.008) and Bayesian change-point methods have detected ion channel gating in a bacterial cell membrane and lithological changes in oil wells.[32](https://ar5iv.labs.arxiv.org/html/2507.01558) Quality-control manufacturing monitoring remains the original application.[4](https://par.nsf.gov/servlets/purl/10492374)\n\n## Limitations and alternatives\n\nUnder independent Gaussian noise all compared changepoint methods perform reasonably well, but heavier-tailed noise or positive autocorrelation leads to over-estimation of the number of changepoints.[8](https://onlinelibrary.wiley.com/doi/10.1002/sta4.291) Default implementations of almost all change-in-mean algorithms, which assume constant mean between changes and independent noise, substantially over-estimate the number of changes when the mean fluctuates locally or noise is autocorrelated.[10](https://ar5iv.labs.arxiv.org/html/2005.01379) Binary segmentation has low power to detect short segments because it adds changes one at a time,[8](https://onlinelibrary.wiley.com/doi/10.1002/sta4.291) and procedures such as CUSUM that are tuned to a specified change size can perform poorly when the actual change magnitude differs from the design value, though they generally retain some power against other nonzero shifts.[33](https://www.jmlr.org/papers/volume24/21-1230/21-1230.pdf) • [34](https://doi.org/10.1080/03610910008813632) In biophysics, hidden Markov models inherently assume geometric holding times, biasing step locations in sparse or noisy data,[28](https://doi.org/10.1016/j.bpj.2024.01.008) and prefiltering improves most methods' performance but blurs closely spaced steps, decreasing temporal resolution.[29](https://pmc.ncbi.nlm.nih.gov/articles/PMC2134886/) Running-mean filtering is fundamentally inadequate for piecewise-constant signals because it must smooth away jumps to remove noise, assumes uncorrelated Gaussian noise, and uses a fixed window that favors certain dwell times.[11](https://pmc.ncbi.nlm.nih.gov/articles/PMC3136774/) Related denoising methods for piecewise-constant signals include total variation regularization, mean shift clustering, running medians, convex clustering shrinkage, and bilateral filtering, most being special cases of a generalized functional, while conventional linear methods are fundamentally unsuited.[18](https://royalsocietypublishing.org/doi/10.1098/rspa.2010.0671)\n\n## References\n\n1. [Selective review of offline change point detection methods (Truong, Oudre, Vayatis)](https://ar5iv.labs.arxiv.org/html/1801.00718)\n2. [A Survey of Methods for Time Series Change Point Detection (Knowledge and Information Systems)](https://eecs.wsu.edu/~cook/pubs/kais16.2.pdf)\n3. [MATH337: Changepoint Detection (lecture notes, Lancaster University)](https://www.lancaster.ac.uk/~romano/teaching/2425MATH337/MATH337--Changepoint-Detection.pdf)\n4. [Sequential change-point detection: Computation versus statistical performance (WIREs Computational Statistics tutorial)](https://par.nsf.gov/servlets/purl/10492374)\n5. [E. S. PAGE (1954). CONTINUOUS INSPECTION SCHEMES. Biometrika.](https://doi.org/10.1093/biomet/41.1-2.100)\n6. [cpop: Detecting Changes in Piecewise-Linear Signals (R package, Journal of Statistical Software)](https://www.jstatsoft.org/article/download/v109i07/4577)\n7. [Detection of Steps in Single Molecule Data](https://pmc.ncbi.nlm.nih.gov/articles/PMC3743561/)\n8. [Relating and comparing methods for detecting changes in mean (Wiley)](https://onlinelibrary.wiley.com/doi/10.1002/sta4.291)\n9. [A note on online change point detection (Wang, Yu, Rinaldo)](https://wrap.warwick.ac.uk/id/eprint/189987/1/A%20note%20on%20online%20change%20point%20detection.pdf)\n10. [Detecting Abrupt Changes in the Presence of Local Fluctuations and Autocorrelated Noise (DeCAFS)](https://ar5iv.labs.arxiv.org/html/2005.01379)\n11. [Steps and Bumps: Precision Extraction of Discrete States of Molecular Machines (Biophysical Journal)](https://pmc.ncbi.nlm.nih.gov/articles/PMC3136774/)\n12. [Advanced algorithm for step detection in single-entity electrochemistry: a comparative study of wavelet transforms and convolutional neural networks (Faraday Discuss., 2025, 257, 384-398, DOI 10.1039/D4FD00130C)](https://pubs.rsc.org/en/content/articlehtml/2025/fd/d4fd00130c)\n13. [Abraham Wald (1946). Differentiation Under the Expectation Sign in the Fundamental Identity of Sequential Analysis. The Annals of Mathematical Statistics.](https://doi.org/10.1214/aoms/1177730889)\n14. [B. Jackson and colleagues (2005). An algorithm for optimal partitioning of data on an interval. IEEE Signal Processing Letters.](https://doi.org/10.1109/lsp.2001.838216)\n15. [F Friedrich and colleagues (2008). Complexity Penalized M-Estimation. Journal of Computational and Graphical Statistics.](https://doi.org/10.1198/106186008x285591)\n16. [R. Killick, P. Fearnhead, I. A. Eckley (2012). Optimal Detection of Changepoints With a Linear Computational Cost. Journal of the American Statistical Association.](https://doi.org/10.1080/01621459.2012.737745)\n17. [Jacob W. J. Kerssemakers and colleagues (2006). Assembly dynamics of microtubules at molecular resolution. Nature.](https://doi.org/10.1038/nature04928)\n18. [Generalized methods and solvers for noise removal from piecewise constant signals. I. Background theory (Proc. R. Soc. A, 2011)](https://royalsocietypublishing.org/doi/10.1098/rspa.2010.0671)\n19. [On optimal multiple changepoint algorithms for large data (Maidstone et al., Statistics and Computing)](https://link.springer.com/article/10.1007/s11222-016-9636-3)\n20. [Catching Change-points with Lasso (NIPS 2007)](https://papers.nips.cc/paper_files/paper/2007/file/e5841df2166dd424a57127423d276bbe-Paper.pdf)\n21. [Piotr Fryzlewicz (2014). Wild binary segmentation for multiple change-point detection. The Annals of Statistics.](https://doi.org/10.1214/14-aos1245)\n22. [A. B. Olshen and colleagues (2004). Circular binary segmentation for the analysis of array-based DNA copy number data. Biostatistics.](https://doi.org/10.1093/biostatistics/kxh008)\n23. [S Kovács and colleagues (2022). Seeded binary segmentation: a general methodology for fast and optimal changepoint detection. Biometrika.](https://doi.org/10.1093/biomet/asac052)\n24. [Birte Eichinger, Claudia Kirch (2017). A MOSUM procedure for the estimation of multiple random change points. Bernoulli.](https://doi.org/10.3150/16-bej887)\n25. [Daniel Barry, J. A. Hartigan (1993). A Bayesian Analysis for Change Point Problems. Journal of the American Statistical Association.](https://doi.org/10.1080/01621459.1993.10594323)\n26. [Adams, Ryan Prescott, MacKay, David J. C. (2007). Bayesian Online Changepoint Detection. arXiv (Cornell University).](https://doi.org/10.48550/arxiv.0710.3742)\n27. [Sean A. McKinney, Chirlmin Joo, Taekjip Ha (2006). Analysis of Single-Molecule FRET Trajectories Using Hidden Markov Modeling. Biophysical Journal.](https://doi.org/10.1529/biophysj.106.082487)\n28. [An accurate probabilistic step finder for time-series analysis (Biophysical Journal, 2024)](https://doi.org/10.1016/j.bpj.2024.01.008)\n29. [A Comparison of Step-Detection Methods: How Well Can You Do? (Biophysical Journal)](https://pmc.ncbi.nlm.nih.gov/articles/PMC2134886/)\n30. [AutoStepfinder: A fast and automated step detection method for single-molecule analysis](https://ceesdekkerlab.nl/wp-content/uploads/2019/08/1-s2.0-S2666389921000829-main.pdf)\n31. [Automated nonparametric method for detection of step-like features in biological data sets (Cytometry Part A, 2015)](https://onlinelibrary.wiley.com/doi/10.1002/cyto.a.22631)\n32. [A Bayesian framework for change-point detection with uncertainty quantification (MICH)](https://ar5iv.labs.arxiv.org/html/2507.01558)\n33. [Fast Online Changepoint Detection via Functional Pruning CUSUM Statistics (FOCuS, JMLR)](https://www.jmlr.org/papers/volume24/21-1230/21-1230.pdf)\n34. [Dbtjmh5qg48 (exa.ai)](https://doi.org/10.1080/03610910008813632)\n\n---\n*Topic: Encyclopedia › Physical world and mathematics › Mathematics and statistics › Statistics and probability › Statistical inference, estimation, sampling, and testing*\n\n*Initially written Sep 29, 2026 · Reviewed: Sep 30, 2026 · Edited: Sep 30, 2026; Oct 15, 2026 · Last review: Sep 30, 2026*\n\n*Copyright 2026 EdgeChat AI, a subsidiary of Biostate AI.*\n\nLicense: Edgepedia Community License 1.0, https://www.edgepedia.org/edgepedia/license\n", "same_as": [ "https://eecs.wsu.edu/~cook/pubs/kais16.2.pdf" ], "url": "https://www.edgepedia.org/step-detection", "markdown_url": "https://www.edgepedia.org/step-detection.md", "license": { "name": "Edgepedia Community License 1.0", "url": "https://www.edgepedia.org/edgepedia/license", "summary": "Free with credit, commercial use included. AI training is open to everyone. For other uses, organizations over USD 100M in revenue or 100M monthly users license separately.", "spdx": "LicenseRef-Edgepedia-Community-1.0" }, "credit": "\"Step detection\", Edgepedia (EdgeChat), https://www.edgepedia.org/step-detection. Edgepedia Community License 1.0.", "credit_md": "\"[Step detection](https://www.edgepedia.org/step-detection)\", Edgepedia (EdgeChat), [https://www.edgepedia.org/step-detection](https://www.edgepedia.org/step-detection). [Edgepedia Community License 1.0](https://www.edgepedia.org/edgepedia/license).", "credit_html": "\"Step detection\", Edgepedia (EdgeChat), https://www.edgepedia.org/step-detection. Edgepedia Community License 1.0.", "speakable": "Step detection is a signal processing and statistical method that locates abrupt steps in a noisy time series, estimating when the signal jumps and each segment's level." }