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The Power of the Pruned Exact Linear Time(PELT) Test in Multiple Changepoint Detection
3 references·8 citing·46 second hop
The Power of the Pruned Exact Linear Time(PELT) Test in Multiple Changepoint Detection (2015)The Power of the Pruned Exact Linear Time…Optimal Detection of Changepoints With a Linear Computational Cost (2012)Optimal Detection of Changepo…Detection of multiple changes in a sequence of dependent variables (1999)Detection of multiple changes…Estimating the Current Mean of a Normal Distribution which is Subjected to Changes in Time (1964)Estimating the Current Mean o…Explainable concept drift in process mining (2023)Explainable concept drift in …The Impacts of COVID-19 and Policies on Spatial and Temporal Distribution Characteristics of Traffic: Two Examples in Beijing (2022)The Impacts of COVID-19 and P…A Deep Learning-Based Cryptocurrency Price Prediction Model That Uses On-Chain Data (2022)A Deep Learning-Based Cryptoc…YouTubing at Home: Media Sharing Behavior Change as Proxy for Mobility Around COVID-19 Lockdowns (2021)YouTubing at Home: Media Shar…Public attitudes toward COVID-19 vaccines on English-language Twitter: A sentiment analysis (2021)Public attitudes toward COVID…Impact of Social Media on Travel Behaviors during the COVID-19 Pandemic: Evidence from New York City (2021)Impact of Social Media on Tra…A Framework for Explainable Concept Drift Detection in Process Mining (2021)A Framework for Explainable C…Social Media Insights Into US Mental Health During the COVID-19 Pandemic: Longitudinal Analysis of Twitter Data (2020)Social Media Insights Into US…CONTINUOUS INSPECTION SCHEMES (1954)A Test for a Change in a Parameter Occurring at an Unknown Point (1955)Inference about the Change-Point from Cumulative Sum Tests (1971)The Equity Premium and Structural Breaks (2000)Dealing with Structural Breaks (2005)Estimating the number of change-points via Schwarz' criterion (1988)Nonparametric Methods in Change-Point Problems (1993)Detection of Abrupt Changes: Theory and Applications. (1995)Detection of multiple change-points in multivariate time series (2006)Structural Breaks in Financial Time Series (2009)A heuristic, iterative algorithm for change-point detection in abrupt change models (2017)Twitter mood predicts the stock market (2011)Mental Health and the Covid-19 Pandemic (2020)Mental Health among Adults during the COVID-19 Pandemic Lockdown: A Cross-Sectional Multi-Country Comparison (2021)Understanding the emotional response to COVID‐19 information in news and social media: A mental health perspective (2021)The bright and dark sides of social media use during COVID-19 lockdown: Contrasting social media effects through social liability vs. social support (2023)Selective review of offline change point detection methods (2019)The COVID-19 social media infodemic (2020)YouTube as a source of information on COVID-19: a pandemic of misinformation? (2020)Some recent development in a concept of causality (1988)Workflow mining: discovering process models from event logs (2004)Process Mining (2016)Action-oriented process mining: bridging the gap between insights and actions (2022)Process-Data Quality: The True Frontier of Process Mining (2023)Tuning Machine Learning to Address Process Mining Requirements (2024)Social Media Use in the United States: Implications for Health Communication (2009)Take Two Aspirin And Tweet Me In The Morning: How Twitter, Facebook, And Other Social Media Are Reshaping Health Care (2009)Attitudes toward COVID-19 Vaccination on Social Media: A Cross-Platform Analysis (2022)Applications of Social Media and Digital Technologies in COVID-19 Vaccination: Scoping Review (2023)HPV vaccine narratives on Twitter during the COVID-19 pandemic: a social network, thematic, and sentiment analysis (2023)Normative Influences on Altruism (1977)The theory of planned behavior (1991)Bir Arastırma Kaynagı Olarak Arsivlenen Sosyal Medya Verilerinin Kullanımı (2021)Evaluation of the impact of COVID-19 pandemic on transportation: a case study of Iran (2022)A survey of methods for time series change point detection (2016)Bitcoin is not the New Gold – A comparison of volatility, correlation, and portfolio performance (2018)A survey of deep learning applications in cryptocurrency (2023)Predicting macro-financial instability – How relevant is sentiment? Evidence from long short-term memory networks (2023)Deep learning for Bitcoin price direction prediction: models and trading strategies empirically compared (2024)Investigating Causal Relations by Econometric Models and Cross-spectral Methods (1969)Fundamentals of Business Process Management (2018)An Experimental Evaluation of Process Concept Drift Detection (2023)Transforming Event Knowledge Graph to Object-Centric Event Logs: A Comparative Study for Multi-dimensional Process Analysis (2023)Machine learning-based detection of concept drift in business processes (2025)changepoint : An R Package for Changepoint Analysis (2014)Wild binary segmentation for multiple change-point detection (2014)
this work references cited by second hop

References

  • 2012Optimal Detection of Changepoints With a Linear Computational Cost2,578 cited
  • 1964Estimating the Current Mean of a Normal Distribution which is Subjected to Changes in Time520 cited
  • 1999Detection of multiple changes in a sequence of dependent variables231 cited

Cited by

  • 2020Social Media Insights Into US Mental Health During the COVID-19 Pandemic: Longitudinal Analysis of Twitter Data271 cited
  • 2021Public attitudes toward COVID-19 vaccines on English-language Twitter: A sentiment analysis136 cited
  • 2022A Deep Learning-Based Cryptocurrency Price Prediction Model That Uses On-Chain Data98 cited
  • 2023Explainable concept drift in process mining41 cited
  • 2021A Framework for Explainable Concept Drift Detection in Process Mining41 cited
  • 2021Impact of Social Media on Travel Behaviors during the COVID-19 Pandemic: Evidence from New York City17 cited
  • 2021YouTubing at Home: Media Sharing Behavior Change as Proxy for Mobility Around COVID-19 Lockdowns12 cited
  • 2022The Impacts of COVID-19 and Policies on Spatial and Temporal Distribution Characteristics of Traffic: Two Examples in Beijing8 cited

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