Bayesian Change Point Detection Research Articles

WiSleep: Smartphone-driven Sleep Population Monitoring with Unsupervised Learning

With sleep deprivation being a public health concern, sleep-monitoring technology, mainly through consumer-grade wearables, has shown value among users to better understand their most fundamental measure of health. Unfortunately, utilizing wearable technology is bound to the conditions of users owning these devices and using them to bed every night. While wearables can deliver highly personalized sleep insights to users, they inadvertently affect the ability of sleep monitoring solutions to reach unprivileged sections of society due to added costs and device accessibility. With our primary motivation to promote sleep monitoring for public health use cases at the population scale, we developed WiSleep , a sleep monitoring system that infers sleep duration from solely relying on a user’s smartphone without requiring a wearable device. Unlike prior efforts that use supervised learning methods and require labelled training data to train sleep models, our method is based on unsupervised learning, which enables easy deployment to new population groups or new regions without a need for labelled data collection and training. Specifically, we employ the smartphone activity of the user, represented by time series of WiFi network event rates, as input data to infer the user’s sleep duration (i.e., sleep time and wake time) through an unsupervised Bayesian change-point detection ensemble model. Our evaluation shows WiSleep ’s efficacy in being a low-cost accessible sleep monitoring approach. We present results that yield comparable performance to prior techniques, particularly those requiring new users’ labeled data to achieve model personalization. System evaluation from a user study achieved an average of 93.68% accuracy within 59 minutes sleep time error, 31 minutes wake time error, and 57 minutes sleep duration error by utilizing coarse-grained time series data. We demonstrate the application of our technique to predict sleep for 1000 anonymous users and enable population-scale analytics with low computational overhead.

Monitoring event data extracted from online news for outbreak detection

Abstract Introduction Digital disease surveillance (DDS) detects public health events from internet-based data e.g., online news. Event features depicting epidemiological and social characteristics of health events can be extracted from news using the natural language process techniques. However, few studies have leveraged the event features to support anomaly detection in DDS. We aimed to understand the distribution of the event features and explore anomaly detection using the frequency of these features. Methods We collected event data from COVID-19-related news collected from October 1 to December 31, 2021, sourced from BioCaster, an infectious-disease-focused DDS system. The predefined event features in BioCaster include disease, pathogen, location and 14 binary features, such as if an event was caused by an unclassified virus. We described the distribution of the features and detected changes in the frequency of event features using a Bayesian online change point detection. We compared the change points with the number of new cases and of genomic samples collected. Results We included 170,168 news articles reporting COVID-19 in 155 countries. The event feature indicating that an event was caused by an unclassified virus was identified as positive among 3831 (2.25%) news and 12.91% of news had positive value for the feature indicating cases who had travelled across international borders. The change points detected from these two features were temporally correlated to the emergence of the Omicron variant in corresponding countries, which was more significant in countries with at least 300 news articles. Conversely, event features irrelevant to this case study, e.g., if the cases were military workers, were identified as negative in all news and no change points were detected. Conclusions Our study highlights the potential of monitoring the frequency of event features extracted from online news for anomaly detection in DDS, which relies on sufficient news coverage. Key messages • Monitoring the event features extracted from online news provide is useful approach for automatic anomaly detection in digital disease surveillance. • Increasing media coverage is fundamental for improving the early detection in a digital disease surveillance system.

Bayesian frameworks for integrating petrologic and geochronologic data

Constraining the absolute time and duration of geologic processes is one of the great challenges and goals in Earth sciences. Increasingly, the integration of geochronologic constraints with petrologic information is being qualitatively applied to understanding the timescales of metamorphic, igneous, tectonic, and fluid-related processes. Many rocks and geochronometers preserve relative age constraints such as compositional zoning or cross cutting relationships. This prior information can be formalized in a Bayesian statistical framework to generate a probabilistic posterior chronology. As we show here, these “age-sequence” models can enhance precision on geochronologic dates and rates and insight into tectonic models. Bayesian modeling of complex, concentrically, zoned monazite from the northern Appalachian orogen was used to develop a detailed temperature-time history through the Acadian (∼405–395 Ma) and Neoacadian (∼380–350 Ma) orogenies with significantly reduced uncertainties (40–70 %). Modeling of zoned monazite from a southern Trans-Hudson orogen granulite yielded durations of 0.5+9/-0.4 Ma and 20+5/-8 Ma for biotite-dehydration melting and suprasolidus conditions, respectively. The relatively short intervals of heating and peak conditions are consistent with a back-arc tectonic setting. A complementary approach, Bayesian change point detection, provides a framework to constrain the timing of compositional changes that can be linked with metamorphic reactions. Applying this approach in the northern Appalachian orogen demonstrates contrasting durations of low-Y monazite crystallization (∼10 vs ∼30 myr) in regions with different pressure-temperature histories. Compositionally distinct monazite domains can be linked with garnet stability, which provides a key constraint on tectonic models. Bayesian statistical analysis represent a powerful tool that can be widely applied to refine the absolute time and duration of geologic processes. A more objective and reproducible set of interpretations are produced by this more formal, although not necessarily complex, statistical analysis.

Development of a Framework for Establishing 'Gold Standard' Outbreak Data from Submitted SARS-CoV-2 Genome Samples.

Submitted genomic data for respiratory viruses reflect the emergence and spread of new variants. Although delays in submission limit the utility of these data for prospective surveillance, they may be useful for evaluating other surveillance sources. However, few studies have investigated the use of these data for evaluating aberration detection in surveillance systems. Our study used a Bayesian online change point detection algorithm (BOCP) to detect increases in the number of submitted genome samples as a means of establishing 'gold standard' dates of outbreak onset in multiple countries. We compared models using different data transformations and parameter values. BOCP detected change points that were not sensitive to different parameter settings. We also found data transformations were essential prior to change point detection. Our study presents a framework for using global genomic submission data to develop 'gold standard' dates about the onset of outbreaks due to new viral variants.

Research on Match Trends in Tennis Competitions Based on Bayesian Online Change Point Detection and Random Forest Prediction Model

Research on Match Trends in Tennis Competitions Based on Bayesian Online Change Point Detection and Random Forest Prediction Model

Spatiotemporal analysis and forecasting of PV systems, battery storage, and EV charging diffusion in California: A graph network approach

Understanding the dynamics of clean energy adoption is crucial for shaping effective energy policies and strategies. To understand the spatiotemporal variability and bottom-up spontaneity of the diffusion of clean energy technologies, this study examined the spatiotemporal diffusion of Photovoltaic (PV) systems, Battery Energy Storage Systems (BESS), and Electric Vehicle Charging Stations (EVCS) across 1773 postcode areas in California over the past 35 years. The Bayesian change point detection algorithm (BCDA) was initially utilized to identify temporal change points in the evolution of these technologies, revealing their diffusion trends over time. Then, the Louvain community detection algorithm (LCDA) was employed to elucidate spatial diffusion patterns across regions, offering insights into the geographic proliferation of clean energy solutions. Furthermore, to forecast future distributions, the spatiotemporal graph convolutional network (STGCN) model was applied, adeptly capturing the multi-stage, nonlinear characteristics of clean energy diffusion marked by significant spatiotemporal interactions. With historical data and spatial proximity, the STGCN model demonstrated fine forecasting precision (R^2 ≥ 0.78). This study validates the effectiveness of a graph network-based approach for analyzing the diffusion of clean energy technologies. It recommends policies tailored to account for technological maturity, geographic disparities, and diffusion stages, aiming for equitable and sustainable clean energy development in the region.

Analysis of trends and abrupt changes in groundwater and meteorological droughts in the United Kingdom

In recent years, the escalating impact of climate change, coupled with growing concerns regarding groundwater drought, has underscored the critical importance of analyzing hydrological trends. This is a foundational step in advancing effective water resource planning and management. This study presents a regional investigation of groundwater and meteorological droughts in the UK, utilizing the Standardized Groundwater Index (SGI) and the Standardized Precipitation Index (SPI). The analysis encompasses drought duration and an evaluation of the correlation between the SGI and SPI. Subsequently, the seasonal Mann-Kendall (MK) test and the Bayesian Changepoint Detection and Time Series Decomposition (BEAST) algorithm were employed to assess overall trends for both SGI and SPI. Additionally, these methods were utilized to pinpoint trend and seasonal abrupt changes within the time series of both indices.The seasonal MK test revealed statistically significant trends in many SGI stations across the UK, spanning from Northern Scotland down to the South of England, encompassing both increasing and decreasing trends. Additionally, the MK test showed slightly positive trends at the low SPI time scale, though these were not statistically significant. In contrast, more consistent and significant increasing trends were observed throughout the entire UK, except for Northern Scotland, which exhibited negative trends. Despite indications of a progressively more humid UK, the BEAST analysis enabled the detection of decreasing abrupt changes in trends. These changes result in sudden transitions from wet to dry conditions, which can be challenging to identify using the MK test alone. However, BEAST revealed both positive and negative abrupt changes across the entire UK, as well as positive or negative variations in seasonality. This suggests a high degree of variability in seasonal patterns. In summary, this study provides a detailed assessment of groundwater and meteorological drought trends and abrupt changes, considering the impact of climate change on various regions of the UK.

A Combined Seasonal Mann–Kendall and Innovative Approach for the Trend Analysis of Streamflow Rate in Two Croatian Rivers

Climate change profoundly impacts hydrological systems, particularly in regions such as Croatia, which is renowned for its diverse geography and climatic variability. This study examined the effect of climate change on streamflow rates in two Croatian rivers: Bednja and Gornja Dobra. Using seasonal Mann–Kendall (MK) tests, overall streamflow trends were evaluated. Additionally, innovative polygon trend analysis (IPTA), innovative visualization for innovative trend analysis (IV-ITA), and Bayesian changepoint detection and time series decomposition (BEAST) algorithms were used to assess the trends’ magnitudes and transitions. The seasonal MK analysis identified significant decreasing trends, primarily during summer. The results of IPTA and IV-ITA revealed consistent decreasing trends throughout most months, with a notable increase in September, especially at high flow values. The rivers’ behavior differed between the first and second halves of the month. BEAST analysis detected abrupt changes, including earlier shifts (1951–1968) in the Bednja and more recent ones (2013–2015) in both the Bednja and, to a lesser extent, the Gornja Dobra rivers. This comprehensive approach enhances our understanding of long-term streamflow trends and short-term fluctuations induced by climate change.

Online learning of order flow and market impact with Bayesian change-point detection methods

Financial order flow exhibits a remarkable level of persistence, wherein buy (sell) trades are often followed by subsequent buy (sell) trades over extended periods. This persistence can be attributed to the division and gradual execution of large orders. Consequently, distinct order flow regimes might emerge, which can be identified through suitable time series models applied to market data. In this paper, we propose the use of Bayesian online change-point detection (BOCPD) methods to identify regime shifts in real-time and enable online predictions of order flow and market impact. To enhance the effectiveness of our approach, we have developed a novel BOCPD method using a score-driven approach. This method accommodates temporal correlations and time-varying parameters within each regime. Through empirical application to NASDAQ data, we have found that: (i) Our newly proposed model demonstrates superior out-of-sample predictive performance compared to existing models that assume i.i.d. behavior within each regime; (ii) When examining the residuals, our model demonstrates good specification in terms of both distributional assumptions and temporal correlations; (iii) Within a given regime, the price dynamics exhibit a concave relationship with respect to time and volume, mirroring the characteristics of actual large orders; (iv) By incorporating regime information, our model produces more accurate online predictions of order flow and market impact compared to models that do not consider regimes.

A Bayesian Change Point Model for Dynamic Alternative Transcription Start Site Usage During Cellular Differentiation.

ABSTRACT An alternative transcription start site (ATSS) is a major driving force for increasing the complexity of transcripts in human tissues. As a transcriptional regulatory mechanism, ATSS has biological significance. Many studies have confirmed that ATSS plays an important role in diseases and cell development and differentiation. However, exploration of its dynamic mechanisms remains insufficient. Identifying ATSS change points during cell differentiation is critical for elucidating potential dynamic mechanisms. For relative ATSS usage as percentage data, the existing methods lack sensitivity to detect the change point for ATSS longitudinal data. In addition, some methods have strict requirements for data distribution and cannot be applied to deal with this problem. In this study, the Bayesian change point detection model was first constructed using reparameterization techniques for two parameters of a beta distribution for the percentage data type, and the posterior distributions of parameters and change points were obtained using Markov Chain Monte Carlo (MCMC) sampling. With comprehensive simulation studies, the performance of the Bayesian change point detection model is found to be consistently powerful and robust across most scenarios with different sample sizes and beta distributions. Second, differential ATSS events in the real data, whose change points were identified using our method, were clustered according to their change points. Last, for each change point, pathway and transcription factor motif analyses were performed on its differential ATSS events. The results of our analyses demonstrated the effectiveness of the Bayesian change point detection model and provided biological insights into cell differentiation.

Assessment of Bayesian Changepoint Detection Methods for Soil Layering Identification Using Cone Penetration Test Data

This paper assesses the effectiveness of different unsupervised Bayesian changepoint detection (BCPD) methods for identifying soil layers, using data from cone penetration tests (CPT). It compares four types of BCPD methods: a previously utilised offline univariate method for detecting clay layers through undrained shear strength data, a newly developed online univariate method, and an offline and an online multivariate method designed to simultaneously analyse multiple data series from CPT. The performance of these BCPD methods was tested using real CPT data from a study area with layers of sandy and clayey soil, and the results were verified against ground-truth data from adjacent borehole investigations. The findings suggest that some BCPD methods are more suitable than others in providing a robust, quick, and automated approach for the unsupervised detection of soil layering, which is critical for geotechnical engineering design.

Bayesian and deep-learning models applied to the early detection of ovarian cancer using multiple longitudinal biomarkers.

Ovarian cancer is the most lethal of all gynecological cancers. Cancer Antigen 125 (CA125) is the best-performing ovarian cancer biomarker which however is still not effective as a screening test in the general population. Recent literature reports additional biomarkers with the potential to improve on CA125 for early detection when using longitudinal multimarker models. Our data comprised 180 controls and 44 cases with serum samples sourced from the multimodal arm of UK Collaborative Trial of Ovarian Cancer Screening (UKCTOCS). Our models were based on Bayesian change-point detection and recurrent neural networks. We obtained a significantly higher performance for CA125-HE4 model using both methodologies (AUC 0.971, sensitivity 96.7% and AUC 0.987, sensitivity 96.7%) with respect to CA125 (AUC 0.949, sensitivity 90.8% and AUC 0.953, sensitivity 92.1%) for Bayesian change-point model (BCP) and recurrent neural networks (RNN) approaches, respectively. One year before diagnosis, the CA125-HE4 model also ranked as the best, whereas at 2 years before diagnosis no multimarker model outperformed CA125. Our study identified and tested different combination of biomarkers using longitudinal multivariable models that outperformed CA125 alone. We showed the potential of multivariable models and candidate biomarkers to increase the detection rate of ovarian cancer.

Analysis of SPI index trend variations in the United Kingdom - A cluster-based and bayesian ensemble algorithms approach

Study regionUnited Kingdom (UK). Study focusA regional investigation of the Standard Precipitation Index (SPI) trends and abrupt changes in the UK has been carried out. The K-means algorithm was employed to partition the study area into six homogeneous regions, each distinguished by specific SPI characteristics. Subsequently, the seasonal Mann-Kendall (MK) test and the Bayesian Changepoint Detection and Time Series Decomposition (BEAST) algorithm were used to evaluate the overall trends for each cluster and SPI time scale, as well as to identify abrupt changes in trend and seasonality along the SPI time series, respectively. New hydrological insights for the regionThe seasonal MK test revealed statistically significant increasing SPI trends for all clusters, except for the southeastern area of the UK, where decreasing, but not statistically significant, SPI trends were observed. Moreover, despite a scenario suggesting an increasingly humid UK, the BEAST analysis allowed the detection of decreasing abrupt changes in trends, resulting in sudden changes from wet to dry conditions, that cannot be identified using the MK test. Alongside these, the BEAST analysis has also revealed positive abrupt changes in trends across all UK, as well as positive or negative variations in seasonality, which are followed by longer or shorter wet or dry periods, respectively. Overall, the study approach provides a detailed picture of the SPI trends and abrupt changes, in light of the impact of climate change on the different areas of the UK.

Unsupervised Bayesian change-point detection approach for reliable prognostics and health management of complex mechanical systems

In the prognostics and health management of mechanical systems, achieving high reliability requires high-quality data for analysis and model training to avoid intensive misalarms and false detections. This research introduces a novel unsupervised Bayesian change-point detection approach specifically tailored to improve the accuracy and dependability of data used in the performance evaluation, fault diagnostics, and predictive maintenance of such systems. By leveraging this method, the precision of change-point detection in operational data is significantly heightened, effectively discerning between normal conditions and potential outliers within the recorded data sequences. The proposed approach replaces the reliance on traditional outlier detection methods with an innovative utilization of prediction error metrics, enriched with statistical analysis to assess and bolster the robustness of the detection algorithm. Conducted case studies on data from the supervisory control and data acquisition (SCADA) system of 92 wind turbines highlight the advanced data cleaning capabilities of our method, which markedly outperforms previous techniques including quartile, Gaussian mixture models, isolation forests, and local outlier factor algorithms. The findings illustrate the methodology’s efficacy in obtaining rcleaned data, which are crucial for the reliable prognostics and health management frameworks of mechanic system, and enhance the confidence in feature extraction and performance prediction.

Spatio-Temporal Dynamics of Total Suspended Sediments in the Belize Coastal Lagoon

Increased tourism in Belize over the last decade and the growth of the local population have led to coastal development and infrastructure expansion. Land use alteration and anthropogenic activity may change the sediment and nutrient loads in coastal systems, which can negatively affect ecosystems via mechanisms such as reducing photosynthetically active radiation fields, smothering sessile habitats, and stimulating eutrophication events. Accurate monitoring and prediction of water quality parameters such as Total Suspended Sediments (TSS), are essential in order to understand the influence of land-based changes, climate, and human activities on the coastal systems and devise strategies to mitigate negative impacts. This study implements machine learning algorithms such as Random Forests (RF), Extreme Gradient Boosting (XGB), and Deep Neural Networks (DNN) to estimate TSS using Sentinel-2 reflectance data in the Belize Coastal Lagoon (BCL) and validates the results using TSS data collected in situ. DNN performed the best and estimated TSS with a testing R2 of 0.89. Time-series analysis was also performed on the BCL’s TSS trends using Bayesian Changepoint Detection (BCD) methods to flag anomalously high TSS spatio-temporally, which may be caused by dredging events. Having such a framework can ease the near-real-time monitoring of water quality in Belize, help track the TSS dynamics for anomalies, and aid in meeting and maintaining the sustainable goals for Belize.

Detecting Global Events with Bayesian Changepoint Detection on Flight Data

As a means of enhancing the intelligence community’s ability to be prepared and prompt in the detection of global events, we present a Bayesian changepoint detection methodology to detect anomalous activity in open-source flight data. We demonstrate a computationally inexpensive methodology to monitor a geographic location for changes in flight activity over time and demonstrate its utility in a case study of the 2022 Russian invasion of Ukraine. Beyond flight data, the methodology described in this paper shows the potential to generate near real-time situational awareness using open-source time series data streams. This paper was previously published and presented in the Donald R. Keith Memorial Capstone Conference at USMA in May of 2023.

A Bayesian change-point detection approach to the economic evaluation of risky projects: an application to healthcare technology assessment

Abstract We propose a Bayesian hypothesis testing framework that allows for the assessment of evidence collected during a clinical trial about the cost-effectiveness of a healthcare technology. The model exploits a Bayesian updating rule that makes the link between the evidence collected in clinical research and the expected payoffs of adoption to the healthcare system. The framework takes into account the cost of decision errors in the payoff function, allowing the decision maker to compute the cost of taking a decision when evidence is far from the optimal decision triggers. We show, using a real-world cost-effectiveness study based on clinical trial evidence, how rules derived from a sequential adaptive design approach can lead to quicker decisions when compared to the value of information decision framework. Our application shows that a sequential approach has the potential to lead to quicker decisions, higher payoffs, and better health outcomes.

Regime Shifts in the Damage Caused by Tropical Cyclones in the Guangdong–Hong Kong–Macao Greater Bay Area of China

Tropical cyclones (TCs) pose a significant threat in terms of wind-induced damage and storm risk to the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) of China. In this research, annual power dissipation index (PDI) and storm surge and wave destructive potential (SDP) index from June to November were used to estimate the damage caused by the TC events in the buffer zone of the GBA. The regime shifts in 1993 and 2013 were identified through the Bayesian changepoint detection in six TC datasets. The TC-induced damage during 1994–2012 (the low-damage period) was weaker than that in 1977–1993 and 2013–2020 (the high-damage periods). The intensity and size of stronger TCs are the dominant factors responsible for the interdecadal changes in the TC damage. This may be explained by the influences of sea surface temperature (SST), surface latent heat flux and mid-level relative humidity. During high-damage periods, TCs can extract more energy from the ocean, leading to increased TC intensity and larger size. Conversely, during low-damage periods, TCs undergo a decrease in energy intake, resulting in reduced TC power and smaller size. The variations in the SST are relative to the Luzon Strait transport. In addition, the reduction in TC translation speed is unfavorable for the development of TCs in low-damage periods. Further research suggested that mid-level steering flow affects the TC movement velocity. The results offer valuable insights into the variations in the TC-induced damage in the GBA.

The robustness of our assumptions about recruitment: A re-examination of marine recruitment dynamics with additional data and novel methods

Recruitment is the number of young fish entering a population and is an essential process in age-structured stock assessment models. Many of the models on which stock assessments are based assume some level of influence of spawning biomass on recruitment, and estimation of this functional relationship has been a focus of substantial research. However, recruitment is also influenced by environmental fluctuations that induce autocorrelated patterns, trends, and shifts in deviations from the stock-recruitment relationship. Consequently, previous meta-analyses of stock-recruitment relationships have had trouble calibrating the relationship. We revisited the findings of Szuwalski et al. (2015) to determine if their results are robust to the addition of data to the RAM Legacy Stock Assessment database and choice of analysis method (the PELT algorithm, MARSS models, and Bayesian change point detection). Since the publication of Szuwalski et al. (2015), the number of stocks in the database has doubled. We determined the primary influence of spawning biomass on recruitment and examined the recruitment time series for regime shifts for 432 stocks. Our results indicated that 57% of stocks did not have a significant correlation between spawning biomass and recruitment over the observed biomasses. Environmental conditions played a larger role in recruitment variation than spawning biomass. The presence, location, and number of regime shifts in recruitment time series was highly dependent on the detection method, with Bayesian change point detection identifying the fewest regime shifts. Despite the sensitivity to the detection method, 46% of stocks without a significant correlation between spawning biomass and recruitment are estimated to have experienced at least one regime shift as determined by the PELT algorithm Our analyses suggest that effective methods for modeling and forecasting large variations in recruitment over time are needed, particularly given that climate change is predicted to impact the frequency and magnitude of regime shifts.

Bayesian Analysis of Change Point Problems Using Conditionally Specified Priors

In data analysis, change point problems correspond to abrupt changes in stochastic mechanisms generating data. The detection of change points is a relevant problem in the analysis and prediction of time series. In this paper, we consider a class of conjugate prior distributions obtained from conditional specification methodology for solving this problem. We illustrate the application of such distributions in Bayesian change point detection analysis with Poisson processes. We obtain the posterior distribution of model parameters using general bivariate distribution with gamma conditionals. Simulation from the posterior are readily implemented using a Gibbs sampling algorithm. The Gibbs sampling is implemented even when using conditional densities that are incompatible or only compatible with an improper joint density. The application of such methods will be demonstrated using examples of simulated and real data.