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Almarashi, Abdullah M., and Khushnoor Khan. "Bayesian Structural Time Series." Nanoscience and Nanotechnology Letters 12, no. 1 (January 1, 2020): 54–61. http://dx.doi.org/10.1166/nnl.2020.3083.
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The current study focused on modeling times series using Bayesian Structural Time Series technique (BSTS) on a univariate data-set. Real-life secondary data from stock prices for flying cement covering a period of one year was used for analysis. Statistical results were based on simulation procedures using Kalman filter and Monte Carlo Markov Chain (MCMC). Though the current study involved stock prices data, the same approach can be applied to complex engineering process involving lead times. Results from the current study were compared with classical Autoregressive Integrated Moving Average (ARIMA) technique. For working out the Bayesian posterior sampling distributions BSTS package run with R software was used. Four BSTS models were used on a real data set to demonstrate the working of BSTS technique. The predictive accuracy for competing models was assessed using Forecasts plots and Mean Absolute Percent Error (MAPE). An easyto-follow approach was adopted so that both academicians and practitioners can easily replicate the mechanism. Findings from the study revealed that, for short-term forecasting, both ARIMA and BSTS are equally good but for long term forecasting, BSTS with local level is the most plausible option.
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Hall, Jamie, Michael K. Pitt, and Robert Kohn. "Bayesian inference for nonlinear structural time series models." Journal of Econometrics 179, no. 2 (April 2014): 99–111. http://dx.doi.org/10.1016/j.jeconom.2013.10.016.
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Wang, Yi-Fu, and Tsai-Hung Fan. "A Bayesian analysis on time series structural equation models." Journal of Statistical Planning and Inference 141, no. 6 (June 2011): 2071–78. http://dx.doi.org/10.1016/j.jspi.2010.12.017.
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Brodersen, Kay H., Fabian Gallusser, Jim Koehler, Nicolas Remy, and Steven L. Scott. "Inferring causal impact using Bayesian structural time-series models." Annals of Applied Statistics 9, no. 1 (March 2015): 247–74. http://dx.doi.org/10.1214/14-aoas788.
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Kalinina, Irina A., and Aleksandr P. Gozhyj. "Modeling and forecasting of nonlinear nonstationary processes based on the Bayesian structural time series." Applied Aspects of Information Technology 5, no. 3 (October 25, 2022): 240–55. http://dx.doi.org/10.15276/aait.05.2022.17.
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The article describes an approach to modelling and forecasting non-linear non-stationary time series for various purposes using Bayesian structural time series. The concepts of non-linearity and non-stationarity, as well as methods for processing non-linearity’sand non-stationarity in the construction of forecasting models are considered. The features of the Bayesian approach in the processing of nonlinearities and nonstationaryare presented. An approach to the construction of probabilistic-statistical models based on Bayesian structural models of time series has been studied. Parametric and non-parametric methods for forecasting non-linear and non-stationary time series are considered. Parametric methods include methods: classical autoregressive models, neural networks, models of support vector machines, hidden Markov models. Non-parametric methods include methods: state-space models, functional decomposition models, Bayesian non-parametric models. One of the types of non-parametric models isBayesian structural time series. The main features of constructing structural time series are considered. Models of structural time series are presented. The process of learning the Bayesianstructural model of time series is described. Training is performed in four stages: setting the structure of the model and a priori probabilities; applying a Kalman filter to update state estimates based on observed data;application of the “spike-and-slab”method to select variables in a structural model; Bayesian averaging to combine the results to make a prediction. An algorithm for constructing a Bayesian structural time seriesmodel is presented. Various components of the BSTS model are considered andanalysed, with the help of which the structures of alternative predictive models are formed. As an example of the application of Bayesian structural time series, the problem of predicting Amazon stock prices is considered. The base dataset is amzn_share. After loading, the structure and data types were analysed, and missing values were processed. The data are characterized by irregular registration of observations, which leads to a large number of missing values and “masking” possible seasonal fluctuations. This makes the task of forecasting rather difficult. To restore gaps in the amzn_sharetime series, the linear interpolation method was used. Using a set of statistical tests (ADF, KPSS, PP), the series was tested for stationarity. The data set is divided into two parts: training and testing. The fitting of structural models of time series was performed using the Kalman filterand the Monte Carlo method according to the Markov chain scheme. To estimate and simultaneously regularize the regression coefficients, the spike-and-slab method was applied. The quality of predictive models was assessed.
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AL-Moders, Ali Hussein, and Tasnim H. Kadhim. "Bayesian Structural Time Series for Forecasting Oil Prices." Ibn AL- Haitham Journal For Pure and Applied Sciences 34, no. 2 (April 20, 2021): 100–107. http://dx.doi.org/10.30526/34.2.2631.
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There are many methods of forecasting, and these methods take data only, analyze it, make a prediction by analyzing, neglect the prior information side and do not considering the fluctuations that occur overtime. The best way to forecast oil prices that takes the fluctuations that occur overtime and is updated by entering prior information is the Bayesian structural time series (BSTS) method. Oil prices fluctuations have an important role in economic so predictions of future oil prices that are crucial for many countries whose economies depend mainly on oil, such as Iraq. Oil prices directly affect the health of the economy. Thus, it is necessary to forecast future oil price with models adapted for emerging events. In this article, we study the Bayesian structural time series (BSTS) for forecasting oil prices. Results show that the price of oil will increase to 156.2$ by 2035.
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Chaturvedi, Anoop, and Jitendra Kumar. "Bayesian Unit Root Test for Time Series Models with Structural Breaks." American Journal of Mathematical and Management Sciences 27, no. 1-2 (January 2007): 243–68. http://dx.doi.org/10.1080/01966324.2007.10737699.
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Fildes, Robert. "Forecasting, Structural Time Series Models and the Kalman Filter: Bayesian Forecasting and Dynamic Models." Journal of the Operational Research Society 42, no. 11 (November 1991): 1031–33. http://dx.doi.org/10.1057/jors.1991.194.
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Kang, Jin-Su, Stephen Thomas Downing, Nabangshu Sinha, and Yi-Chieh Chen. "Advancing Causal Inference: Differences-in-Differences vs. Bayesian Structural Time Series Models." Academy of Management Proceedings 2021, no. 1 (August 2021): 15410. http://dx.doi.org/10.5465/ambpp.2021.15410abstract.
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Jeong, Chulwoo, and Jaehee Kim. "Bayesian multiple structural change-points estimation in time series models with genetic algorithm." Journal of the Korean Statistical Society 42, no. 4 (December 2013): 459–68. http://dx.doi.org/10.1016/j.jkss.2013.02.001.
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Kumar, Saurabh, Jitendra Kumar, Vikas Kumar Sharma, and Varun Agiwal. "Random order autoregressive time series model with structural break." Model Assisted Statistics and Applications 15, no. 3 (October 9, 2020): 225–37. http://dx.doi.org/10.3233/mas-200490.
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This paper deals with the problem of modelling time series data with structural breaks occur at multiple time points that may result in varying order of the model at every structural break. A flexible and generalized class of Autoregressive (AR) models with multiple structural breaks is proposed for modelling in such situations. Estimation of model parameters are discussed in both classical and Bayesian frameworks. Since the joint posterior of the parameters is not analytically tractable, we employ a Markov Chain Monte Carlo method, Gibbs sampling to simulate posterior sample. To verify the order change, a hypotheses test is constructed using posterior probability and compared with that of without breaks. The methodologies proposed here are illustrated by means of simulation study and a real data analysis.
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Shively, Thomas S., and Robert Kohn. "A Bayesian approach to model selection in stochastic coefficient regression models and structural time series models." Journal of Econometrics 76, no. 1-2 (January 1997): 39–52. http://dx.doi.org/10.1016/0304-4076(95)01781-x.
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Liu, Yuefei, and Xueping Fan. "Time-Independent Reliability Analysis of Bridge System Based on Mixed Copula Models." Mathematical Problems in Engineering 2016 (2016): 1–13. http://dx.doi.org/10.1155/2016/2720614.
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The actual structural systems have many failure modes. Due to the same random sources owned by the performance functions of these failure modes, there usually exist some nonlinear correlations between the various failure modes. How to handle the nonlinear correlations is one of the main scientific problems in the field of structural system reliability. In this paper, for the two-component systems and multiple-component systems with multiple failure modes, the mixed copula models for time-independent reliability analysis of series systems, parallel systems, series-parallel systems, and parallel-series systems are presented. These obtained mixed copula models, considering the nonlinear correlation between failure modes, are obtained with the chosen optimal copula functions with the Bayesian selection criteria and Monte Carlo Sampling (MCS) method. And a numerical example is provided to illustrate the feasibility and application of the built mixed models for structural system reliability.
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Peters, Angus Ewan, Vitomir Racic, Stana Živanović, and John Orr. "Fourier Series Approximation of Vertical Walking Force-Time History through Frequentist and Bayesian Inference." Vibration 5, no. 4 (December 9, 2022): 883–913. http://dx.doi.org/10.3390/vibration5040052.
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The increased ambition of architects coupled with advancements in structural materials, as well as the rapidly increasing pressure on civil engineering sector to reduce embodied carbon, have resulted in longer spans and more slender pedestrian structures. These structures often have one or more low natural frequencies in the range of human walking accompanied with low modal masses and damping ratios. Thus, they are prone to excessive and often resonant vibrations that may compromise the serviceability limit state. Principally the uncertainty in prediction of the vibration serviceability limit state mainly originates from unreliable estimates of pedestrian loading. The key rationale behind this situation is the limited mathematical characterisation featuring in current design codes and guidelines pertinent to pedestrian-induced loading. The Fourier approximation is typically used to describe individual walking forces. Historically, such models are based on limited experimental data and deterministic mathematical descriptions. Current industry used load models featured in design codes and guidelines have been shown to incorporate inherent bias through limited intra-subject variation and poor correlation with real walking loads. This paper presents an improved Fourier model of vertical walking force across multiple harmonics, presented in a Bayesian and Frequentist statistical parameterisation. They are derived using the most comprehensive dataset to date, comprising of over ten hours of continuous vertical walking force signals. Dissimilar to previous Fourier models, the proposed models attempt to encapsulate the surround energy leakage around harmonic integers with a singular value. The proposed models provide consistently lower force amplitudes than any previous model and is shown to be more representative of real walking. The proposed model provides a closer approximation of a structural acceleration than any other similar Fourier-based model. The proposed model provides further evidence to combine the so called high and low frequency load models.
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Feroze, Navid, Kamran Abbas, Farzana Noor, and Amjad Ali. "Analysis and forecasts for trends of COVID-19 in Pakistan using Bayesian models." PeerJ 9 (July 7, 2021): e11537. http://dx.doi.org/10.7717/peerj.11537.
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Background COVID-19 is currently on full flow in Pakistan. Given the health facilities in the country, there are serious threats in the upcoming months which could be very testing for all the stakeholders. Therefore, there is a need to analyze and forecast the trends of COVID-19 in Pakistan. Methods We have analyzed and forecasted the patterns of this pandemic in the country, for next 30 days, using Bayesian structural time series models. The causal impacts of lifting lockdown have also been investigated using intervention analysis under Bayesian structural time series models. The forecasting accuracy of the proposed models has been compared with frequently used autoregressive integrated moving average models. The validity of the proposed model has been investigated using similar datasets from neighboring countries including Iran and India. Results We observed the improved forecasting accuracy of Bayesian structural time series models as compared to frequently used autoregressive integrated moving average models. As far as the forecasts are concerned, on August 10, 2020, the country is expected to have 333,308 positive cases with 95% prediction interval [275,034–391,077]. Similarly, the number of deaths in the country is expected to reach 7,187 [5,978–8,390] and recoveries may grow to 279,602 [208,420–295,740]. The lifting of lockdown has caused an absolute increase of 98,768 confirmed cases with 95% interval [85,544–111,018], during the post-lockdown period. The positive aspect of the forecasts is that the number of active cases is expected to decrease to 63,706 [18,614–95,337], on August 10, 2020. This is the time for the concerned authorities to further restrict the active cases so that the recession of the outbreak continues in the next month.
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Liu, Jason, Daniel J. Spakowicz, Garrett I. Ash, Rebecca Hoyd, Rohan Ahluwalia, Andrew Zhang, Shaoke Lou, et al. "Bayesian structural time series for biomedical sensor data: A flexible modeling framework for evaluating interventions." PLOS Computational Biology 17, no. 8 (August 23, 2021): e1009303. http://dx.doi.org/10.1371/journal.pcbi.1009303.
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The development of mobile-health technology has the potential to revolutionize personalized medicine. Biomedical sensors (e.g., wearables) can assist with determining treatment plans for individuals, provide quantitative information to healthcare providers, and give objective measurements of health, leading to the goal of precise phenotypic correlates for genotypes. Even though treatments and interventions are becoming more specific and datasets more abundant, measuring the causal impact of health interventions requires careful considerations of complex covariate structures, as well as knowledge of the temporal and spatial properties of the data. Thus, interpreting biomedical sensor data needs to make use of specialized statistical models. Here, we show how the Bayesian structural time series framework, widely used in economics, can be applied to these data. This framework corrects for covariates to provide accurate assessments of the significance of interventions. Furthermore, it allows for a time-dependent confidence interval of impact, which is useful for considering individualized assessments of intervention efficacy. We provide a customized biomedical adaptor tool, MhealthCI, around a specific implementation of the Bayesian structural time series framework that uniformly processes, prepares, and registers diverse biomedical data. We apply the software implementation of MhealthCI to a structured set of examples in biomedicine to showcase the ability of the framework to evaluate interventions with varying levels of data richness and covariate complexity and also compare the performance to other models. Specifically, we show how the framework is able to evaluate an exercise intervention’s effect on stabilizing blood glucose in a diabetes dataset. We also provide a future-anticipating illustration from a behavioral dataset showcasing how the framework integrates complex spatial covariates. Overall, we show the robustness of the Bayesian structural time series framework when applied to biomedical sensor data, highlighting its increasing value for current and future datasets.
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Bednar, Ondrej. "The Causal Impact of the Rapid Czech Interest Rate Hike on the Czech Exchange Rate Assessed by the Bayesian Structural Time Series Model." International Journal of Economic Sciences 10, no. 2 (December 20, 2021): 1–17. http://dx.doi.org/10.52950/es.2021.10.2.001.
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I have employed the Bayesian Structural Time Series model to assess the recent interest rate hike by the Czech Central Bank and its causal impact on the Koruna exchange rate. By forecasting exchange rate time series in the absence of the intervention we can subtract the observed values from the prediction and estimate the causal effect. The results show that the impact was little and time limited in one model specification and none in the second version. It implies that the Czech Central Bank possesses the ability to diverge significantly from the Eurozone benchmark interest rate at least in the short term. It also shows that the interest rate hike will not be able to curb global inflation forces on the domestic price level.
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Agbenyega, Diana Ayorkor, John Andoh, Samuel Iddi, and Louis Asiedu. "Modelling Customs Revenue in Ghana Using Novel Time Series Methods." Applied Computational Intelligence and Soft Computing 2022 (April 18, 2022): 1–8. http://dx.doi.org/10.1155/2022/2111587.
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Governments across the world rely on their Customs Administration to provide functions that include border security, intellectual property rights protection, environmental protection, and revenue mobilisation amongst others. Analyzing the trends in revenue being collected from Customs is necessary to direct government policies and decisions. Models that can capture the trends being purported from the nominal (nonreal) tax values with respect to the trade volumes (value) over the period are indispensable. Predominant amongst the existing models are the econometric models (the GDP-based model, the monthly receipts model, and the microsimulation model), which are laborious and sometimes unreliable when studying trends in time series data. In this study, we modelled monthly revenue data obtained from the Ghana Revenue Authority-Customs Division (GRA-CD) for the period January 2010 to December 2019 using two traditional time series models, ARIMA model and ARIMA Error Regression Model (ARIMAX), and two machine learning time series models, Bayesian Structural Time Series (BSTS) model and a Neural Network Autoregression model. The Neural Network Autoregression model of the form NNAR (1, 3) provided the best forecasts with the least Mean Squared Error (MSE) of 53.87 and relatively lower Mean Absolute Percentage Error (MAPE) of 0.08. Generally, the machine learning models (NNAR (1, 3) and BSTS) outperformed the traditional time series models (ARIMA and ARIMAX models). The forecast values from the NNAR (1, 3) indicated a potential decline in revenue and this emphasizes the need for relevant authorities to institute measures to improve revenue generation in the immediate future.
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Saldaña-Zepeda, Dayna P., Ciro Velasco-Cruz, and Víctor H. Torres-Preciado. "Variable Selection in Switching Dynamic Regression Models." Revista Colombiana de Estadística 45, no. 1 (January 1, 2022): 231–63. http://dx.doi.org/10.15446/rce.v45n1.85385.
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Complex dynamic phenomena in which dynamics is related to events (modes) that cause structural changes over time, are well described by the switching linear dynamical system (SLDS). We extend the SLDS by allowing the measurement noise to be mode-specific, a flexible way to model non stationary data. Additionally, for models that are functions of explanatory variables, we adapt a variable selection method to identify which of them are significant in each mode. Our proposed model is a flexible Bayesian nonparametric model that allows to learn about the number of modes and their location, and within each mode, it identifies the significant variables and estimates the regression coefficients. The model performance is evaluated by simulation and two application examples from a dataset of meteorological time series of Barranquilla, Colombia are presented.
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Chien, Lung-Chang, and Shrikant I. Bangdiwala. "The implementation of Bayesian structural additive regression models in multi-city time series air pollution and human health studies." Stochastic Environmental Research and Risk Assessment 26, no. 8 (January 22, 2012): 1041–51. http://dx.doi.org/10.1007/s00477-012-0562-4.
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Drachal, Krzysztof, and Daniel González Cortés. "Estimation of Lockdowns’ Impact on Well-Being in Selected Countries: An Application of Novel Bayesian Methods and Google Search Queries Data." International Journal of Environmental Research and Public Health 20, no. 1 (December 27, 2022): 421. http://dx.doi.org/10.3390/ijerph20010421.
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Lockdowns introduced in connection with the COVID-19 pandemic have had a significant impact on societies from an economic, psychological, and health perspective. This paper presents estimations of their impact on well-being, understood both from the perspective of mental health and considering economic security and similar factors. This is not an easy task because well-being is influenced by numerous factors and the changes happen dynamically. Moreover, there are some obstacles when using the control group. However, other studies show that in certain cases it is possible to approximate selected phenomena with Google search queries data. Secondly, the econometric issues related to the suitable modeling of such a problem can be solved, for example, by using Bayesian methods. In particular, herein the recently gaining in popularity Bayesian structural time series and Bayesian dynamic mixture models are used. Indeed, these methods have not been used in social sciences extensively. However, in the fields where they have been used, they have been very efficient. Especially, they are useful when short time series are analyzed and when there are many variables that potentially have a significant explanatory impact on the response variable. Finally, 15 culturally different and geographically widely scattered countries are analyzed (i.e., Belgium, Brazil, Canada, Chile, Colombia, Denmark, France, Germany, Italy, Japan, Mexico, the Netherlands, Spain, Sweden, and the United Kingdom). Little evidence of any substantial changes in the Internet search intensity on terms connected with negative aspects of well-being and mental health issues is found. For example, in Mexico, some evidence of a decrease in well-being after lockdown was found. However, in Italy, there was weak evidence of an increase in well-being. Nevertheless, the Bayesian structural time series method has been found to fit the data most accurately. Indeed, it was found to be a superior method for causal analysis over the commonly used difference-in-differences method or Bayesian dynamic mixture models.
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Perles-Ribes, José Francisco, Ana Belén Ramón-Rodríguez, and Armando Ortuño Padilla. "Brexit Announcement: Immediate Impact on British Tourism in Spain." Cornell Hospitality Quarterly 60, no. 2 (May 29, 2018): 97–103. http://dx.doi.org/10.1177/1938965518777699.
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The United Kingdom constitutes the principal tourist source market for Spain. This research note analyzes the immediate impact of the Brexit on British tourism in Spain using the Bayesian structural time series models framework. The results obtained show that between July 2016 and September 2017, Brexit has not produced any initial negative effect on the arrival of British tourists or on their spending in Spain.
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Li, Cong, Shui-Hua Jiang, Jinhui Li, and Jinsong Huang. "Bayesian Approach for Sequential Probabilistic Back Analysis of Uncertain Geomechanical Parameters and Reliability Updating of Tunneling-Induced Ground Settlements." Advances in Civil Engineering 2020 (June 24, 2020): 1–13. http://dx.doi.org/10.1155/2020/8528304.
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This paper proposes a new sequential probabilistic back analysis approach for probabilistically determining the uncertain geomechanical parameters of shield tunnels by using time-series monitoring data. The approach is proposed based on the recently developed Bayesian updating with subset simulation. Within the framework of the proposed approach, a complex Bayesian back analysis problem is transformed into an equivalent structural reliability problem based on subset simulation. Hermite polynomial chaos expansion-based surrogate models are constructed to improve the computational efficiency of probabilistic back analysis. The reliability of tunneling-induced ground settlements is updated in the process of sequential back analyses. A real shield tunnel project of No. 1 Nanchang Metro Line in China is investigated to assess the effectiveness of the approach. The proposed approach is able to infer the posterior distributions of uncertain geomechanical parameters (i.e., Young’s moduli of surrounding soil layers and ground vehicle load). The reliability of tunneling-induced ground settlements can be updated in a real-time manner by fully utilizing the time-series monitoring data. The results show good agreement with the variation trend of field monitoring data of ground settlement and the post-event investigations.
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Fan, Xueping, Zhipeng Shang, Guanghong Yang, Xiaoxiong Zhao, and Yuefei Liu. "Incorporation of Structural Health Monitoring Coupled Data in Dynamic Extreme Stress Prediction of Steel Bridges Using Dynamic Coupled Linear Models." Advances in Civil Engineering 2020 (August 11, 2020): 1–12. http://dx.doi.org/10.1155/2020/8712907.
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In this article, an approach for using structural health monitoring coupled extreme stress data in dynamic extreme stress prediction of steel bridges is presented, where the coupled extreme stress data means the extreme stress data with dynamicity, randomness, and trend. Firstly, the modeling processes about dynamic coupled linear models (DCLM) are provided based on a supposed coupled time series; furthermore, the dynamic probabilistic recursion processes about DCLM are given with Bayes method; secondly, the monitoring dynamic coupled extreme stress data is taken as a time series, historical monitoring coupled extreme stress data-based DCLM and the corresponding Bayesian probabilistic recursion processes are given for predicting bridge extreme stresses; furthermore, the monitoring mechanism is provided for monitoring the prediction precision of DCLM; finally, the monitoring coupled extreme stress data of a steel bridge is used to illustrate the proposed approach which can provide the foundations for bridge reliability prediction and assessment.
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Xia, Chunlian, and Jing Huang. "Construction of Inflation Forecasting Model Based on Ensemble Empirical Mode Decomposition and Bayesian Model." Journal of Sensors 2022 (July 14, 2022): 1–10. http://dx.doi.org/10.1155/2022/8275259.
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The high explanatory power of the first-order lag term of inflation in the inflation explanatory factor is that, on the one hand, the calculation of annual inflation indicators makes the inflation values of adjacent months cover the high correlation caused by the common price increase, and on the other hand, it also shows that people’s perception of inflation is high. Some adaptability is expected. Although there are many Bayesian models available, due to the limitation of high-dimensional characteristics of the economy, most of the current inflation forecasting researches focus on a variety of generalized naive Bayesian models. By summarizing and analyzing the structural characteristics, learning methods, and classification principles of different Bayesian models, this paper finds out the important factors that affect the performance of the models and provides a theoretical basis for further improving the performance of Bayesian inflation forecasting. In this paper, the empirical mode decomposition method is introduced into inflation forecasting, and EEMD has obvious advantages in dealing with nonstationary and nonlinear time series and can decompose the signal according to the time scale characteristics of the data itself. Decompose the original time series step by step to generate eigenmode functions with different time scales. It is divided into high-frequency sequence and low-frequency sequence. Use rolling method and iterative method to construct subsamples for the sample data in this sample interval, forecast the inflation rate of each subsample interval in the next 12 months, and then compare the predicted value with the actual value to obtain a certain constructive conclusion. The predicted value is relatively close to the real value, which has theoretical and practical significance, and the predicted results obtained have no obvious regularity, but the root mean square error can be kept within 60%. By comparing the predicted value and the actual value, it can be seen that the prediction effect of the EEMD model is better.
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Amador-Jiménez, Luis Esteban, and Donath Mrawira. "Capturing variability in pavement performance models from sufficient time-series predictors: a case study of the New Brunswick road network." Canadian Journal of Civil Engineering 38, no. 2 (February 2011): 210–20. http://dx.doi.org/10.1139/l10-127.
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This paper proposes the use of multi-level Bayesian modeling for calibrating mechanistic model parameters from historical data while capturing reliability by estimating a desired confidence interval of the predictions. The model is capable of estimating the parameters from the observed data and expert criteria even in cases of missing data points. This approach allows rapid generation of several deterioration models without the need to partition the data into pavement families. It estimates posterior distributions for model coefficients and predicts values of the response for unobserved levels of the causal factors. A case study from the New Brunswick Department of Transportation is used to calibrate a simplified mechanistic pavement roughness progression model based on 6-year international roughness index (IRI) observations. The model incorporates the effects of pavement structural capacity in terms of deflection basin parameter (AREA) in place of the modified structural number, traffic loading (ESAL) and environmental factors. The results of the model showed that, as expected, chipseal roads have higher as built roughness and deteriorate faster than asphalt roads. Sensitivity analysis of the deterministic (the mean predictions) part of the model showed that in New Brunswick where traffic is relatively low the environment is the most important factor.
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Fan, Xueping, and Yuefei Liu. "Dynamic Reliability Prediction of Bridges Based on Decoupled SHM Extreme Stress Data and Improved BDLM." Advances in Civil Engineering 2021 (June 7, 2021): 1–9. http://dx.doi.org/10.1155/2021/5579368.
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Bridge health monitoring system has produced a huge amount of monitored data (extreme stress data, etc.) in the long-term service periods; how to reasonably predict structural dynamic reliability with these data is one key problem in structural health monitoring (SHM) field. In this paper, considering the coupling, randomness, and time variation of SHM data, firstly, the coupled extreme stress data, which are considered as a time series, are decoupled into high-frequency and low-frequency data with the moving average method. Secondly, Bayesian dynamic linear models (BDLM) without priori monitoring error data (e.g., unknown monitored error variance) are built to dynamically predict the decoupled extreme stress; furthermore, the dynamic reliability of bridge members is predicted with the built BDLM and first-order second moment (FOSM) reliability method. Finally, an actual example is provided to illustrate the feasibility and application of the proposed models and methods. The research results of this paper will provide the theoretical foundations for structural reliability prediction.
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Smets, Frank, and Rafael Wouters. "Shocks and Frictions in US Business Cycles: A Bayesian DSGE Approach." American Economic Review 97, no. 3 (May 1, 2007): 586–606. http://dx.doi.org/10.1257/aer.97.3.586.
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Using a Bayesian likelihood approach, we estimate a dynamic stochastic general equilibrium model for the US economy using seven macroeconomic time series. The model incorporates many types of real and nominal frictions and seven types of structural shocks. We show that this model is able to compete with Bayesian Vector Autoregression models in out-of-sample prediction. We investigate the relative empirical importance of the various frictions. Finally, using the estimated model, we address a number of key issues in business cycle analysis: What are the sources of business cycle fluctuations? Can the model explain the cross correlation between output and inflation? What are the effects of productivity on hours worked? What are the sources of the “Great Moderation”? (JEL D58, E23, E31, E32)
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Feroze, Navid. "Forecasting the patterns of COVID-19 and causal impacts of lockdown in top five affected countries using Bayesian Structural Time Series Models." Chaos, Solitons & Fractals 140 (November 2020): 110196. http://dx.doi.org/10.1016/j.chaos.2020.110196.
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Kleszcz, Agnieszka, and Krzysztof Rusek. "Has EU Accession Boosted Patent Performance in the EU-13? A Critical Evaluation Using Causal Impact Analysis with Bayesian Structural Time-Series Models." Forecasting 4, no. 4 (October 29, 2022): 866–81. http://dx.doi.org/10.3390/forecast4040047.
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This paper provides new insights into the causal effects of the enlargement of the European Union (EU) on patent performance. The study focuses on the new EU member states (EU-13) and accession is considered as an intervention whose causal effect is estimated by the causal impact method using a Bayesian structural time-series model (proposed by Google). The empirical results based on data collected from the OECD database from 1985–2017 point towards a conclusion that joining the EU has had a significant impact on patent performance in Romania, Estonia, Poland, the Czech Republic, Croatia and Lithuania, although in the latter two countries, the impact was negative. For the rest of the EU-13 countries, there is no significant effect on patent performance. Whether the EU accession effect is significant or not, the EU-13 are far behind the EU-15 (countries which entered the EU before 2004) in terms of patent performance. The majority of patents (98.66%) are assigned to the EU-15, with just 1.34% of assignees belonging to the EU-13.
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Sobieraj, Janusz, and Dominik Metelski. "Application of the Bayesian New Keynesian DSGE Model to Polish Macroeconomic Data." Engineering Economics 32, no. 2 (April 29, 2021): 140–53. http://dx.doi.org/10.5755/j01.ee.32.2.27214.
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In the paper we estimate a simple New Keynesian Dynamic Stochastic General Equilibrium NK DSGE model on the basis of Polish macro data from the period 2000-2019. The model is specified similarly to Galí (2008) with the use of the Bayesian approach. The NK DSGE model combines the advantages of both structural models and time-series models and, therefore, shows a significant degree of alignment with empirical data. The Bayesian estimation is based on the prior distribution of the model input parameters, which are later compared with the posteriors. The results obtained allow for assessing the persistence of responses to technological, inflationary and monetary policy shocks. On the basis of the NK DSGE model, we formulate a perception of macroeconomic interactions, e.g. nominal interest rates’ association with inflation and the output gap. In other words, the NK DSGE model provides a better understanding of the relationship between interest rates, inflation and the output gap. This in turn makes it easier to understand the monetary policy response function.
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Kim, Dayeong, Sun Bean Kim, Soyoung Jeon, Subin Kim, Kyoung Hwa Lee, Hye Sun Lee, and Sang Hoon Han. "No Change of Pneumocystis jirovecii Pneumonia after the COVID-19 Pandemic: Multicenter Time-Series Analyses." Journal of Fungi 7, no. 11 (November 19, 2021): 990. http://dx.doi.org/10.3390/jof7110990.
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Consolidated infection control measures imposed by the government and hospitals during COVID-19 pandemic resulted in a sharp decline of respiratory viruses. Based on the issue of whether Pneumocystis jirovecii could be transmitted by airborne and acquired from the environment, we assessed changes in P. jirovecii pneumonia (PCP) cases in a hospital setting before and after COVID-19. We retrospectively collected data of PCP-confirmed inpatients aged ≥18 years (N = 2922) in four university-affiliated hospitals between January 2015 and June 2021. The index and intervention dates were defined as the first time of P. jirovecii diagnosis and January 2020, respectively. We predicted PCP cases for post-COVID-19 and obtained the difference (residuals) between forecasted and observed cases using the autoregressive integrated moving average (ARIMA) and the Bayesian structural time-series (BSTS) models. Overall, the average of observed PCP cases per month in each year were 36.1 and 47.3 for pre- and post-COVID-19, respectively. The estimate for residuals in the ARIMA model was not significantly different in the total PCP-confirmed inpatients (7.4%, p = 0.765). The forecasted PCP cases by the BSTS model were not significantly different from the observed cases in the post-COVID-19 (−0.6%, 95% credible interval; −9.6~9.1%, p = 0.450). The unprecedented strict non-pharmacological interventions did not affect PCP cases.
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Clark-Moorman, Kyleigh, Jason Rydberg, and Edmund F. McGarrell. "Impact Evaluation of a Parolee-Based Focused Deterrence Program on Community-Level Violence." Criminal Justice Policy Review 30, no. 9 (November 27, 2018): 1408–30. http://dx.doi.org/10.1177/0887403418812999.
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We estimate the impact of a parolee-based focused deterrence (“pulling levers”) intervention on community-level firearm and non-firearm violence in Rockford, Illinois, via a retrospective, quasi-experimental design. Focusing on incidents of firearm violence in Rockford over a period of 60 months (38 months pre-intervention, 22 months post-intervention), program impact is assessed using Bayesian Structural Time Series (BSTS) models, constructing a synthetic control-based counterfactual time series from National Incident-Based Reporting System (NIBRS) data from 59 non-treated cities of similar size. Relative to the synthetic control counterfactual, the intervention was associated with significant reductions in both firearm and non-firearm violence, particularly robberies, ranging from 6% to 30%. Consistent with research at other sites, these findings support the notion that focused deterrence strategies centered on high-risk parolees may result in reductions in firearm violence at the community level. The BSTS approach is a useful application for producing counterfactuals in retrospective quasi-experimental impact evaluations.
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Ma, Hye Hyun, and Byoungkwan Lee. "Applying Bayesian Structural Time-series Models to Estimate the Effectiveness of Public Advertising: Causal Impact of Television Anti-Smoking Campaign on Tobacco Sales in Korea." KOREAN JOURNAL OF ADVERTISING 33, no. 8 (November 30, 2022): 7–30. http://dx.doi.org/10.14377/kja.2022.11.30.7.
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Maiti, Saumen, and Ram Krishna Tiwari. "Automatic discriminations among geophysical signals via the Bayesian neural networks approach." GEOPHYSICS 75, no. 1 (January 2010): E67—E78. http://dx.doi.org/10.1190/1.3298501.
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The precise classification of changes in rock boundaries/facies from well-log records is a complex problem in geophysical data processing. Observed well-log data are a complex superposition of nonstationary/nonlinear signals of varying wavelengths and frequencies, shaped by the heterogeneous composition and structural variation of rock types in the earth. This impairs our ability to use traditional statistical techniques, which in most cases fail to discriminate and/or, at best, do not precisely extract facies changes from complex well-log signals. We propose a new method, set in a Bayesian neural network (BNN) framework and using a powerful hybrid Monte Carlo simulation scheme to identify facies changes from complex well-log data. We first construct a complex, composite, synthetic time series using the data from three simple models: first-order autoregressive, logistic, and random white noise. Then we attempt to identify individualsignals in the pooled synthetic time series. We use the autocorrelation and the spectral characteristics of the individual signals as input vectors for training, validating, and testing the artificial neural network model. The results show that the Bayesian separation scheme provides consistently good results, with accuracy at more than 74%. When the method was tested using well-log data from the German Continental Deep Drilling Program (KTB), it was able to discriminate boundaries of lithofacies with an accuracy of approximately 92% in validation and 93% in test samples. The efficacy of the BNN in the presence of colored noise suggests that the designed network topology is robust for up to 30% correlated noise; however, adding more noise (say, 50% or more) obscures the desired signals. Our method provides a robust means for decoding finely detailed successions of lithofacies from complex well-log data, better describing the nature of the underlying inhomogeneous crust.
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Jeon, Young-Eun, Suk-Bok Kang, and Jung-In Seo. "Hybrid Predictive Modeling for Charging Demand Prediction of Electric Vehicles." Sustainability 14, no. 9 (April 30, 2022): 5426. http://dx.doi.org/10.3390/su14095426.
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In recent years, the supply of electric vehicles, which are eco-friendly cars that use electric energy rather than fossil fuels, which cause air pollution, is increasing. Accordingly, it is emerging as an urgent task to predict the charging demand for the smooth supply of electric energy required to charge electric vehicle batteries. In this paper, to predict the charging demand, time series analysis is performed based on two types of frames: One is using traditional time series techniques such as dynamic harmonic regression, seasonal and trend decomposition using Loess, and Bayesian structural time series. The other is the most widely used machine learning techniques, including random forest and extreme gradient boosting. However, the tree-based machine learning approaches have the disadvantage of not being able to capture the trend, so a hybrid strategy is proposed to overcome this problem. In addition, the seasonal variation is reflected as the feature by using the Fourier transform which is useful in the case of describing the seasonality patterns of time series data with multiple seasonality. The considered time series models are compared and evaluated through various accuracy measures. The experimental results show that the machine learning approach based on the hybrid strategy generally achieves significant improvements in predicting the charging demand. Moreover, when compared with the original machine learning method, the prediction based on the proposed hybrid strategy is more accurate than that based on the original machine learning method. Based on these results, it can find out that the proposed hybrid strategy is useful for smoothly planning future power supply and demand and efficiently managing electricity grids.
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Harris, Glen R. "Markov Chain Monte Carlo Estimation of Regime Switching Vector Autoregressions." ASTIN Bulletin 29, no. 1 (May 1999): 47–79. http://dx.doi.org/10.2143/ast.29.1.504606.
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AbstractFinancial time series data are typically found to possess leptokurtic frequency distributions, time varying volatilities, outliers and correlation structures inconsistent with linear generating processes, nonlinear dependence, and dependencies between series that are not stable over time. Regime Switching Vector Autoregressions are of interest because they are capable of explaining the observed features of the data, can capture a variety of interactions between series, appear intuitively reasonable, are vector processes, and are now tractable.This paper considers a vector autoregression subject to periodic structural changes. The parameters of a vector autoregression are modelled as the outcome of an unobserved discrete Markov process with unknown transition probabilities. The unobserved regimes, one for each time point, together with the regime transition probabilities, are determined in addition to the vector autoregression parameters within each regime.A Bayesian Markov Chain Monte Carlo estimation procedure is developed which efficiently generates the posterior joint density of the parameters and the regimes. The complete likelihood surface is generated at the same time, enabling estimation of posterior model probabilities for use in non-nested model selection. The procedure can readily be extended to produce joint prediction densities for the variables, incorporating both parameter and model uncertainty.Results using simulated and real data are provided. A clear separation of the variance between a stable and an unstable regime was observed. Ignoring regime shifts is very likely to produce misleading volatility estimates and is unlikely to be robust to outliers. A comparison with commonly used models suggests that Regime Switching Vector Autoregressions provide a particularly good description of the observed data.
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Eun, Sang Jun. "Effects of stricter drunk-driving laws on alcohol-related road traffic death, injury, and crash rates in South Korea: A synthetic counterfactual approach using Bayesian structural time-series models." Accident Analysis & Prevention 163 (December 2021): 106455. http://dx.doi.org/10.1016/j.aap.2021.106455.
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Moore, Hollie, Brian Broderick, and Breiffni Fitzgerald. "Experimental and computational evaluation of modal identification techniques for structural damping estimation." E3S Web of Conferences 347 (2022): 01008. http://dx.doi.org/10.1051/e3sconf/202234701008.
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The damping ratio is a key indicator of a structures susceptability to human discomfort due to dynamic loads. Computational models, wind tunnel studies and empirical data can provide estimates of the damping ratio of a structure. However, the only true way to investigate the damping ratio of a structure is through modal identification using data from field tests carried out on the full-scale finished structure. This paper investigates the efficacy of three modal identification methods for estimating the damping ratio of the first two modes of a structure from ambient data. The three methods considered are the Bayesian Fast Fourier Transform (BFFT), the Random Decrement Technique (RDT), and a hybrid of the RDT which first decomposes the ambient data into sub signals using Analytical Mode Decomposition (AMD) and is referred to as the AMD-RDT. Each method is applied to two case studies in order to investigate the accuracy of their damping estimates. The first case study is experimental and involves the excitation of a scaled model structure using a shake table; the second case study considers a computational model of a tall building under simulated wind loads. It was found that the AMD-RDT was the superior method for damping estimation, particularly for the estimation of damping ratio in the second mode, even when the modes were closely spaced. The length of time series data used and the noise within the data was seen to affect the accuracy of the three methods studied.
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Hoang, Nam, and Terrance Grieb. "Hedging positions in US wheat markets: a disaggregated data analysis." Studies in Economics and Finance 37, no. 3 (May 13, 2020): 429–55. http://dx.doi.org/10.1108/sef-08-2019-0329.
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Purpose This study aims to spot wheat data and disaggregated commitment of trader data for CME traded wheat futures to examine the effect of exogenous shocks for hedging positions of Producers and Swap Dealers on cash-futures basis and excess futures returns. Design/methodology/approach A Bayesian vector autoregression (BVAR) methodology is used to capture volatility transfer effects. Findings Evidence is presented that institutional short hedging positions play a major role in the pricing of asymmetric information held by Swap Dealers into the basis. The results also indicate that producer hedging contains information when conditions in the supply chain create a shift in long vs short hedging demand. Finally, the results demonstrate that that Swap Dealer short hedging has the greatest effect on risk premium size and historical volatility. Originality/value Various proxies for spot prices are used in the literature, although actual spot price data is not common. In addition, stationarity for basis and open interest data is induced using the Baxter-King filter which allows us to work with levels, rather than percentage changes, in the time series data. This provides the ability to directly observe the effect of outright open interest positions for hedgers on contemporaneous innovations in basis and in excess returns. The use of a BVAR methodology represents an improvement over other structural VAR models by capturing contemporaneous systemic effects within an endogeneity based structural framework.
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LeSage, James P., and Andrew Solocha. "The Impact Of Weekly Money Supply Announcements ON Stock Market Returns: A Multiprocess Mixture Model Approach." Journal of Applied Business Research (JABR) 9, no. 3 (September 29, 2011): 100. http://dx.doi.org/10.19030/jabr.v9i3.6045.
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This study provides evidence concerning the impact of anticipated and unanticipated components of the weekly money supply announcements on stock market returns in the United States and Canada on the date after the announcement. The innovative aspect of this study is the use of a multiprocess mixture model recently proposed by Gordon and Smith (1990) for modeling time series that are subject to several forms of potential discontinuous change and outliers. The technique involves running multiple models in parallel with recursive Bayesian updating procedures which extend the standard Kalman filter. The results provide strong evidence in favor of the efficient markets hypothesis that only the unanticipated component of the money supply announcement influences the stock market returns in both the United States and Canada.The use of OLS estimated in the present study produces results which suggest that both anticipated and unanticipated components of the money supply announcement exert a statistically significant influence on stock market returns in both countries. In contract, the multiprocess mixture model estimation method produces results which support the efficient markets hypothesis. The difference in findings between OLS and multiprocess estimation methods is attributed to the ability of the multiprocess techniques to model discontinuous structural shifts in the parameters and accommodate outliers in the stock return-weekly money relationship. The multiprocess mixture method provides evidence that numerous discontinuities and outliers exist in the stock market returns-weekly money relation and produces posterior probabilities for the multiple models running in parallel. These probabilities suggest that the OLS model has low posterior probability relative to the structural shift and outlier models which suggest poor inferences regarding the significance of anticipated and unanticipated money arise from the use of OLS estimation techniques.
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Carpenter, Chris. "Bayesian Framework Helps History Matching of Permian EOR Field With Data Uncertainty." Journal of Petroleum Technology 75, no. 01 (January 1, 2023): 59–61. http://dx.doi.org/10.2118/0123-0059-jpt.
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_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 200807, “Closing the Loop on a History Match for a Permian EOR Field With Relative Permeability Data Uncertainty,” by Usman Aslam, Emerson, and Jorge Burgos, SPE, and Craig Williams, Occidental Petroleum, et al. The paper has not been peer reviewed. _ Reservoir production forecasts are inherently uncertain because of the lack of quality data available to build predictive reservoir models. Traditionally, a best estimate for relative permeability data is assumed during the history-matching process despite significant uncertainty. Performing sensitivities around the best-estimate relative permeability case will cover only part of the uncertainty space. In the complete paper, the authors present an application of a Bayesian framework for uncertainty assessment and efficient history matching of a Permian carbon-dioxide (CO2) enhanced oil recovery field for reliable production forecasting. Field Details Regional and Structural Geology. The Central Basin Platform (CBP) is a positive tectonic feature that separates the Delaware and Midland sub-basins of the Permian. The study field is located on the northeastern end of the CBP, with the Permian (Guadalupian) -aged reservoir composed of San Andres Formation dolostone. The total thickness of the unit is approximately 1,500 ft, with the main reservoir within the middle 600 ft. Structural Framework. The area of interest features 270 wells that have a total depth in the San Andres formation or deeper. Of these, 234 have digital open- or cased-hole logs that were used to correlate formation tops. After reviewing all well logs, it became clear that, historically, the zones were primarily identifiable from porosity picks, which are more subjective than markers identified with gamma ray methods. A new sequence stratigraphic framework was developed based on core descriptions and outcrop analogs. This correlation framework was then extrapolated to well logs. After a grid of north/south and west/east cross sections was correlated across the field and a series of loop ties was made for quality-control purposes, structure and isopach maps were created for each zone. Reservoir Description. At the time of discovery, natural gas was trapped at the structural high point of the study field. Above the gas/oil interface is the gas cap. Below the gas was an oil accumulation, which extended to the producing oil/water contact (POWC). The POWC was defined by early drilling as the maximum depth where water-free oil was produced. The base of the transition zone is the top of the residual oil zone (ROZ); this reservoir interval extends to the free-water level and is an interval believed to have been waterflooded naturally. The ROZ is the target for CO2 injection. Grid Construction and Property Modeling. A full-field detailed 3D reservoir characterization was modeled at 50-ft × 50-ft grid increments and an approximate 2-ft average layer thickness. The reservoir model consists of 114,222,528 cells. A total of 235 wells in the unit area was correlated and used to constrain the structural framework of the model. Quantitative porosity log suites were available in 233 of the wells in the model area, which were used to create the full-field porosity property in the 3D model. The logs were normalized to the core data and upscaled.
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Swiderski, Alexander M., Yina M. Quique, Michael Walsh Dickey, and William D. Hula. "Treatment of Underlying Forms: A Bayesian Meta-Analysis of the Effects of Treatment and Person-Related Variables on Treatment Response." Journal of Speech, Language, and Hearing Research 64, no. 11 (November 8, 2021): 4308–28. http://dx.doi.org/10.1044/2021_jslhr-21-00131.
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Purpose This meta-analysis synthesizes published studies using “treatment of underlying forms” (TUF) for sentence-level deficits in people with aphasia (PWA). The study aims were to examine group-level evidence for TUF efficacy, to characterize the effects of treatment-related variables (sentence structural family and complexity; treatment dose) in relation to the Complexity Account of Treatment Efficacy (CATE) hypothesis, and to examine the effects of person-level variables (aphasia severity, sentence comprehension impairment, and time postonset of aphasia) on TUF response. Method Data from 13 single-subject, multiple-baseline TUF studies, including 46 PWA, were analyzed. Bayesian generalized linear mixed-effects interrupted time series models were used to assess the effect of treatment-related variables on probe accuracy during baseline and treatment. The moderating influence of person-level variables on TUF response was also investigated. Results The results provide group-level evidence for TUF efficacy demonstrating increased probe accuracy during treatment compared with baseline phases. Greater amounts of TUF were associated with larger increases in accuracy, with greater gains for treated than untreated sentences. The findings revealed generalization effects for sentences that were of the same family but less complex than treated sentences. Aphasia severity may moderate TUF response, with people with milder aphasia demonstrating greater gains compared with people with more severe aphasia. Sentence comprehension performance did not moderate TUF response. Greater time postonset of aphasia was associated with smaller improvements for treated sentences but not for untreated sentences. Conclusions Our results provide generalizable group-level evidence of TUF efficacy. Treatment and generalization responses were consistent with the CATE hypothesis. Model results also identified person-level moderators of TUF (aphasia severity, time postonset of aphasia) and preliminary estimates of the effects of varying amounts of TUF for treated and untreated sentences. Taken together, these findings add to the TUF evidence and may guide future TUF treatment–candidate selection. Supplemental Material https://doi.org/10.23641/asha.16828630
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Brunner, Dominik, Tim Arnold, Stephan Henne, Alistair Manning, Rona L. Thompson, Michela Maione, Simon O'Doherty, and Stefan Reimann. "Comparison of four inverse modelling systems applied to the estimation of HFC-125, HFC-134a, and SF<sub>6</sub> emissions over Europe." Atmospheric Chemistry and Physics 17, no. 17 (September 11, 2017): 10651–74. http://dx.doi.org/10.5194/acp-17-10651-2017.
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Abstract. Hydrofluorocarbons (HFCs) are used in a range of industrial applications and have largely replaced previously used gases (CFCs and HCFCs). HFCs are not ozone-depleting but have large global warming potentials and are, therefore, reported to the United Nations Framework Convention on Climate Change (UNFCCC). Here, we use four independent inverse models to estimate European emissions of the two HFCs contributing the most to global warming (HFC-134a and HFC-125) and of SF6 for the year 2011. Using an ensemble of inverse models offers the possibility to better understand systematic uncertainties in inversions. All systems relied on the same measurement time series from Jungfraujoch (Switzerland), Mace Head (Ireland), and Monte Cimone (Italy) and the same a priori estimates of the emissions, but differed in terms of the Lagrangian transport model (FLEXPART, NAME), inversion method (Bayesian, extended Kalman filter), treatment of baseline mole fractions, spatial gridding, and a priori uncertainties. The model systems were compared with respect to the ability to reproduce the measurement time series, the spatial distribution of the posterior emissions, uncertainty reductions, and total emissions estimated for selected countries. All systems were able to reproduce the measurement time series very well, with prior correlations between 0.5 and 0.9 and posterior correlations being higher by 0.05 to 0.1. For HFC-125, all models estimated higher emissions from Spain + Portugal than reported to UNFCCC (median higher by 390 %) though with a large scatter between individual estimates. Estimates for Germany (+140 %) and Ireland (+850 %) were also considerably higher than UNFCCC, whereas the estimates for France and the UK were consistent with the national reports. In contrast to HFC-125, HFC-134a emissions from Spain + Portugal were broadly consistent with UNFCCC, and emissions from Germany were only 30 % higher. The data suggest that the UK over-reports its HFC-134a emissions to UNFCCC, as the model median emission was significantly lower, by 50 %. An overestimation of both HFC-125 and HFC-134a emissions by about a factor of 2 was also found for a group of eastern European countries (Czech Republic + Poland + Slovakia), though with less confidence since the measurement network has a low sensitivity to these countries. Consistent with UNFCCC, the models identified Germany as the highest national emitter of SF6 in Europe, and the model median emission was only 1 % lower than the UNFCCC numbers. In contrast, the model median emissions were 2–3 times higher than UNFCCC numbers for Italy, France, and Spain + Portugal. The country-aggregated emissions from the different models often did not overlap within the range of the analytical uncertainties formally given by the inversion systems, suggesting that parametric and structural uncertainties are often dominant in the overall a posteriori uncertainty. The current European network of three routine monitoring sites for synthetic greenhouse gases has the potential to identify significant shortcomings in nationally reported emissions, but a denser network would be needed for more reliable monitoring of country-wide emissions of these important greenhouse gases across Europe.
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Lawhun Costello, Victoria, Guillaume Chevance, David Wing, Shadia J. Mansour-Assi, Sydney Sharp, Natalie M. Golaszewski, Elizabeth A. Young, et al. "Impact of the COVID-19 Pandemic on Objectively Measured Physical Activity and Sedentary Behavior Among Overweight Young Adults: Yearlong Longitudinal Analysis." JMIR Public Health and Surveillance 7, no. 11 (November 24, 2021): e28317. http://dx.doi.org/10.2196/28317.
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Background The COVID-19 pandemic has impacted multiple aspects of daily living, including behaviors associated with occupation, transportation, and health. It is unclear how these changes to daily living have impacted physical activity and sedentary behavior. Objective In this study, we add to the growing body of research on the health impact of the COVID-19 pandemic by examining longitudinal changes in objectively measured daily physical activity and sedentary behavior among overweight or obese young adults participating in an ongoing weight loss trial in San Diego, California. Methods Data were collected from 315 overweight or obese (BMI: range 25.0-39.9 kg/m2) participants aged from 18 to 35 years between November 1, 2019, and October 30, 2020, by using the Fitbit Charge 3 (Fitbit LLC). After conducting strict filtering to find valid data on consistent wear (>10 hours per day for ≥250 days), data from 97 participants were analyzed to detect multiple structural changes in time series of physical activity and sedentary behavior. An algorithm was designed to detect multiple structural changes. This allowed for the automatic identification and dating of these changes in linear regression models with CIs. The number of breakpoints in regression models was estimated by using the Bayesian information criterion and residual sum of squares; the optimal segmentation corresponded to the lowest Bayesian information criterion and residual sum of squares. To quantify the changes in each outcome during the periods identified, linear mixed effects analyses were conducted. In terms of key demographic characteristics, the 97 participants included in our analyses did not differ from the 210 participants who were excluded. Results After the initiation of the shelter-in-place order in California on March 19, 2021, there were significant decreases in step counts (−2872 steps per day; 95% CI −2734 to −3010), light physical activity times (−41.9 minutes; 95% CI −39.5 to −44.3), and moderate-to-vigorous physical activity times (−12.2 minutes; 95% CI −10.6 to −13.8), as well as significant increases in sedentary behavior times (+52.8 minutes; 95% CI 47.0-58.5). The decreases were greater than the expected declines observed during winter holidays, and as of October 30, 2020, they have not returned to the levels observed prior to the initiation of shelter-in-place orders. Conclusions Among overweight or obese young adults, physical activity times decreased and sedentary behavior times increased concurrently with the implementation of COVID-19 mitigation strategies. The health conditions associated with a sedentary lifestyle may be additional, unintended results of the COVID-19 pandemic.
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Sajedi, Seyedomid, and Xiao Liang. "Trident: A Deep Learning Framework for High-Resolution Bridge Vibration Monitoring." Applied Sciences 12, no. 21 (October 30, 2022): 10999. http://dx.doi.org/10.3390/app122110999.
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Bridges are the essential components in lifeline transportation systems, and their safe operation is of great importance. Information on structural damage could assist in timely repairs and reduce downtime. With the latest advancements in sensing technology, collecting vibration data from bridges has become more accessible. However, effective vibration processing is still a challenge, given the high dimensionality and massive size of vibration data. Existing studies have shown that machine/deep learning techniques can be valuable tools for this task. However, the learning and computational capacities of these models are challenged in the presence of large sensor arrays. We propose Trident as a novel deep learning framework that enables automatic damage feature extraction by simultaneously learning from temporal and three-dimensional (3D) spatial variations of 6D input data in instrumented bridges. Trident is equipped with 3 ConvLSTM3D branches to achieve this goal. A 3D steel truss bridge subject to dynamic traffic loads is monitored for its vibrations to evaluate Trident’s robustness in finding damaged elements. A damage dataset of 52,800 vehicle passing simulations is generated leveraging a database of 528 passenger vehicles in the United States, obtained from the National Highway and Traffic Safety Administration. Bayesian optimization is utilized to tune the model’s hyperparameters, achieving a test Node Average Geometric Mean Accuracy of 86%. This level of performance is promising given the high dimensionality and complexities of the output space in vibration-based monitoring. Trident’s concept can be extended to other vibration monitoring tasks with different time series data and damage labeling strategies.
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Krzysztofowicz, Roman. "BAYESIAN MODELS OF FORECASTED TIME SERIES." Journal of the American Water Resources Association 21, no. 5 (October 1985): 805–14. http://dx.doi.org/10.1111/j.1752-1688.1985.tb00174.x.
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Menichetti, Lorenzo, Thomas Kätterer, and Jens Leifeld. "Parametrization consequences of constraining soil organic matter models by total carbon and radiocarbon using long-term field data." Biogeosciences 13, no. 10 (May 23, 2016): 3003–19. http://dx.doi.org/10.5194/bg-13-3003-2016.
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Abstract. Soil organic carbon (SOC) dynamics result from different interacting processes and controls on spatial scales from sub-aggregate to pedon to the whole ecosystem. These complex dynamics are translated into models as abundant degrees of freedom. This high number of not directly measurable variables and, on the other hand, very limited data at disposal result in equifinality and parameter uncertainty. Carbon radioisotope measurements are a proxy for SOC age both at annual to decadal (bomb peak based) and centennial to millennial timescales (radio decay based), and thus can be used in addition to total organic C for constraining SOC models. By considering this additional information, uncertainties in model structure and parameters may be reduced. To test this hypothesis we studied SOC dynamics and their defining kinetic parameters in the Zürich Organic Fertilization Experiment (ZOFE) experiment, a > 60-year-old controlled cropland experiment in Switzerland, by utilizing SOC and SO14C time series. To represent different processes we applied five model structures, all stemming from a simple mother model (Introductory Carbon Balance Model – ICBM): (I) two decomposing pools, (II) an inert pool added, (III) three decomposing pools, (IV) two decomposing pools with a substrate control feedback on decomposition, (V) as IV but with also an inert pool. These structures were extended to explicitly represent total SOC and 14C pools. The use of different model structures allowed us to explore model structural uncertainty and the impact of 14C on kinetic parameters. We considered parameter uncertainty by calibrating in a formal Bayesian framework. By varying the relative importance of total SOC and SO14C data in the calibration, we could quantify the effect of the information from these two data streams on estimated model parameters. The weighing of the two data streams was crucial for determining model outcomes, and we suggest including it in future modeling efforts whenever SO14C data are available. The measurements and all model structures indicated a dramatic decline in SOC in the ZOFE experiment after an initial land use change in 1949 from grass- to cropland, followed by a constant but smaller decline. According to all structures, the three treatments (control, mineral fertilizer, farmyard manure) we considered were still far from equilibrium. The estimates of mean residence time (MRT) of the C pools defined by our models were sensitive to the consideration of the SO14C data stream. Model structure had a smaller effect on estimated MRT, which ranged between 5.9 ± 0.1 and 4.2 ± 0.1 years and 78.9 ± 0.1 and 98.9 ± 0.1 years for young and old pools, respectively, for structures without substrate interactions. The simplest model structure performed the best according to information criteria, validating the idea that we still lack data for mechanistic SOC models. Although we could not exclude any of the considered processes possibly involved in SOC decomposition, it was not possible to discriminate their relative importance.
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Florens, J. P., M. Mouchart, and J. F. Richard. "Structural time series modeling: a Bayesian approach." Applied Mathematics and Computation 20, no. 3-4 (November 1986): 365–400. http://dx.doi.org/10.1016/0096-3003(86)90012-3.
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Scott, Steven L., and Hal R. Varian. "Predicting the present with Bayesian structural time series." International Journal of Mathematical Modelling and Numerical Optimisation 5, no. 1/2 (2014): 4. http://dx.doi.org/10.1504/ijmmno.2014.059942.
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