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Journal articles on the topic 'Dynamic model averaging'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 April 2022

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1

Koop, Gary, and Dimitris Korobilis. "FORECASTING INFLATION USING DYNAMIC MODEL AVERAGING*." International Economic Review 53, no. 3 (2012): 867–86. http://dx.doi.org/10.1111/j.1468-2354.2012.00704.x.

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2

Onorante, Luca, and Adrian E. Raftery. "Dynamic model averaging in large model spaces using dynamic Occam׳s window." European Economic Review 81 (January 2016): 2–14. http://dx.doi.org/10.1016/j.euroecorev.2015.07.013.

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3

Mahmud, Md Rasel, Ahmed F. Abdou, and Hemanshu Pota. "Stability Analysis of Grid-Connected Photovoltaic Systems with Dynamic Phasor Model." Electronics 8, no. 7 (2019): 747. http://dx.doi.org/10.3390/electronics8070747.

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The typical layout of power systems is experiencing significant change, due to the high penetration of renewable energy sources (RESs). The ongoing evaluation of power systems is expecting more detailed and accurate mathematical modeling approaches for RESs which are dominated by power electronics. Although modeling techniques based on state–space averaging (SSA) have traditionally been used to mathematically represent the dynamics of power systems, the performance of such a model-based system degrades under high switching frequency. The multi-frequency averaging (MFA)-based higher-index dynamic phasor modeling tool is proposed in this paper, which is entirely new and can provide better estimations of dynamics. Dynamic stability analysis is presented in this paper for the MFA-based higher-index dynamical model of single-stage single-phase (SSSP) grid-connected photovoltaic (PV) systems under different switching frequencies.

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4

Koop, Gary, and Simon Potter. "Forecasting in dynamic factor models using Bayesian model averaging." Econometrics Journal 7, no. 2 (2004): 550–65. http://dx.doi.org/10.1111/j.1368-423x.2004.00143.x.

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5

McCormick, Tyler H., Adrian E. Raftery, David Madigan, and Randall S. Burd. "Dynamic Logistic Regression and Dynamic Model Averaging for Binary Classification." Biometrics 68, no. 1 (2011): 23–30. http://dx.doi.org/10.1111/j.1541-0420.2011.01645.x.

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6

Styrin, Konstantin. "Forecasting Inflation in Russia Using Dynamic Model Averaging." Russian Journal of Money and Finance 78, no. 1 (2019): 03–18. http://dx.doi.org/10.31477/rjmf.201901.03.

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7

Yang, Hongxia, Jonathan R. M. Hosking, and Yasuo Amemiya. "Dynamic Latent Class Model Averaging for Online Prediction." Journal of Forecasting 34, no. 1 (2014): 1–14. http://dx.doi.org/10.1002/for.2315.

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8

Aye, Goodness, Rangan Gupta, Shawkat Hammoudeh, and Won Joong Kim. "Forecasting the price of gold using dynamic model averaging." International Review of Financial Analysis 41 (October 2015): 257–66. http://dx.doi.org/10.1016/j.irfa.2015.03.010.

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9

Bork, Lasse, and Stig V. Møller. "Forecasting house prices in the 50 states using Dynamic Model Averaging and Dynamic Model Selection." International Journal of Forecasting 31, no. 1 (2015): 63–78. http://dx.doi.org/10.1016/j.ijforecast.2014.05.005.

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10

Chen, Yong, Huiling Yuan, Yize Yang, and Ruochen Sun. "Sub-daily soil moisture estimate using dynamic Bayesian model averaging." Journal of Hydrology 590 (November 2020): 125445. http://dx.doi.org/10.1016/j.jhydrol.2020.125445.

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11

Geweke, John, and Gianni Amisano. "Prediction with Misspecified Models." American Economic Review 102, no. 3 (2012): 482–86. http://dx.doi.org/10.1257/aer.102.3.482.

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The assumption that one of a set of prediction models is a literal description of reality formally underlies many formal econometric methods, including Bayesian model averaging and most approaches to model selection. Prediction pooling does not invoke this assumption and leads to predictions that improve on those based on Bayesian model averaging, as assessed by the log predictive score. The paper shows that the improvement is substantial using a pool consisting of a dynamic stochastic general equilibrium model, a vector autoregression, and a dynamic factor model, in conjunction with standard US postwar quarterly macroeconomic time series.

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12

Liu, Bin. "Instantaneous Frequency Tracking under Model Uncertainty via Dynamic Model Averaging and Particle Filtering." IEEE Transactions on Wireless Communications 10, no. 6 (2011): 1810–19. http://dx.doi.org/10.1109/twc.2011.042211.100639.

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13

Jeřábek, Tomáš, and Radka Šperková. "A Predictive Likelihood Approach to Bayesian Averaging." Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 63, no. 4 (2015): 1269–76. http://dx.doi.org/10.11118/actaun201563041269.

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Multivariate time series forecasting is applied in a wide range of economic activities related to regional competitiveness and is the basis of almost all macroeconomic analysis. In this paper we combine multivariate density forecasts of GDP growth, inflation and real interest rates from four various models, two type of Bayesian vector autoregression (BVAR) models, a New Keynesian dynamic stochastic general equilibrium (DSGE) model of small open economy and DSGE-VAR model. The performance of models is identified using historical dates including domestic economy and foreign economy, which is represented by countries of the Eurozone. Because forecast accuracy of observed models are different, the weighting scheme based on the predictive likelihood, the trace of past MSE matrix, model ranks are used to combine the models. The equal-weight scheme is used as a simple combination scheme. The results show that optimally combined densities are comparable to the best individual models.

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14

Horbach, Marc, та Gerd Schön. "Disorder-averaging and the dynamic nonlinear σ-model of localization theory". Physica B: Condensed Matter 165-166 (серпень 1990): 315–16. http://dx.doi.org/10.1016/s0921-4526(90)81007-b.

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15

Chen, Jia, Degui Li, Oliver Linton, and Zudi Lu. "Semiparametric Ultra-High Dimensional Model Averaging of Nonlinear Dynamic Time Series." Journal of the American Statistical Association 113, no. 522 (2018): 919–32. http://dx.doi.org/10.1080/01621459.2017.1302339.

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16

Liu, Jing, Yu Wei, Feng Ma, and M. I. M. Wahab. "Forecasting the realized range-based volatility using dynamic model averaging approach." Economic Modelling 61 (February 2017): 12–26. http://dx.doi.org/10.1016/j.econmod.2016.11.020.

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17

Wei, Yu, and Yang Cao. "Forecasting house prices using dynamic model averaging approach: Evidence from China." Economic Modelling 61 (February 2017): 147–55. http://dx.doi.org/10.1016/j.econmod.2016.12.002.

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18

Horbach, M. "Disorder-averaging and the dynamic nonlinear σ-model of localization theory". Physica B: Condensed Matter 165-166 (серпень 1990): 315–16. http://dx.doi.org/10.1016/0921-4526(90)90549-a.

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19

Nonejad, Nima. "AN OVERVIEW OF DYNAMIC MODEL AVERAGING TECHNIQUES IN TIME‐SERIES ECONOMETRICS." Journal of Economic Surveys 35, no. 2 (2021): 566–614. http://dx.doi.org/10.1111/joes.12410.

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20

Kirkpatrick, M. P., A. S. Ackerman, D. E. Stevens, and N. N. Mansour. "On the Application of the Dynamic Smagorinsky Model to Large-Eddy Simulations of the Cloud-Topped Atmospheric Boundary Layer." Journal of the Atmospheric Sciences 63, no. 2 (2006): 526–46. http://dx.doi.org/10.1175/jas3651.1.

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Abstract In this paper the dynamic Smagorinsky model originally developed for engineering flows is adapted for simulations of the cloud-topped atmospheric boundary layer in which an anelastic form of the governing equations is used. The adapted model accounts for local buoyancy sources, vertical density stratification, and poor resolution close to the surface and calculates additional model coefficients for the subgrid-scale fluxes of potential temperature and total water mixing ratio. Results obtained with the dynamic model are compared with those obtained using two nondynamic models for simulations of a nocturnal marine stratocumulus cloud deck observed during the first research flight of the second Dynamics and Chemistry of Marine Stratocumulus (DYCOMS-II) field experiment. The dynamic Smagorinsky model is found to give better agreement with the observations for all parameters and statistics. The dynamic model also gives improved spatial convergence and resolution independence over the nondynamic models. The good results obtained with the dynamic model appear to be due primarily to the fact that it calculates minimal subgrid-scale fluxes at the inversion. Based on other results in the literature, it is suggested that entrainment in the DYCOMS-II case is due predominantly to isolated mixing events associated with overturning internal waves. While the behavior of the dynamic model is consistent with this entrainment mechanism, a similar tendency to switch off subgrid-scale fluxes at an interface is also observed in a case in which gradient transport by small-scale eddies has been found to be important. This indicates that there may be problems associated with the application of the dynamic model close to flow interfaces. One issue here involves the plane-averaging procedure used to stabilize the model, which is not justified when the averaging plane intersects a deforming interface. More fundamental, however, is that the behavior may be due to insufficient resolution in this region of the flow. The implications of this are discussed with reference to both dynamic and nondynamic subgrid-scale models, and a new approach to turbulence modeling for large-eddy simulations is proposed.

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21

Chen, Huigang, Alin Mirestean, and Charalambos G. Tsangarides. "Bayesian model averaging for dynamic panels with an application to a trade gravity model." Econometric Reviews 37, no. 7 (2016): 777–805. http://dx.doi.org/10.1080/07474938.2016.1167857.

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22

Mirestean, Alin, Charalambos G. Tsangarides, and Huigang Chen. "Limited Information Bayesian Model Averaging for Dynamic Panels with Short Time Periods." IMF Working Papers 09, no. 74 (2009): 1. http://dx.doi.org/10.5089/9781451872217.001.

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23

Risse, Marian, and Martin Kern. "Forecasting house-price growth in the Euro area with dynamic model averaging." North American Journal of Economics and Finance 38 (November 2016): 70–85. http://dx.doi.org/10.1016/j.najef.2016.08.001.

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24

Drachal, Krzysztof. "Forecasting unemployment rate in Poland with dynamic model averaging and internet searches." Global Business and Economics Review 23, no. 4 (2020): 368. http://dx.doi.org/10.1504/gber.2020.10031721.

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25

Drachal, Krzysztof. "Forecasting unemployment rate in Poland with dynamic model averaging and internet searches." Global Business and Economics Review 23, no. 4 (2020): 368. http://dx.doi.org/10.1504/gber.2020.110684.

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26

Wang, Yudong, Feng Ma, Yu Wei, and Chongfeng Wu. "Forecasting realized volatility in a changing world: A dynamic model averaging approach." Journal of Banking & Finance 64 (March 2016): 136–49. http://dx.doi.org/10.1016/j.jbankfin.2015.12.010.

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27

Muglia, Camilla, Luca Santabarbara, and Stefano Grassi. "Is Bitcoin a Relevant Predictor of Standard & Poor’s 500?" Journal of Risk and Financial Management 12, no. 2 (2019): 93. http://dx.doi.org/10.3390/jrfm12020093.

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The paper investigates whether Bitcoin is a good predictor of the Standard & Poor’s 500 Index. To answer this question we compare alternative models using a point and density forecast relying on Dynamic Model Averaging (DMA) and Dynamic Model Selection (DMS). According to our results, Bitcoin does not show any direct impact on the predictability of Standard & Poor’s 500 for the considered sample.

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28

Raftery, Adrian E., Miroslav Kárný, and Pavel Ettler. "Online Prediction Under Model Uncertainty via Dynamic Model Averaging: Application to a Cold Rolling Mill." Technometrics 52, no. 1 (2010): 52–66. http://dx.doi.org/10.1198/tech.2009.08104.

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29

Mehrnegar, Nooshin, Owen Jones, Michael Bliss Singer, Maike Schumacher, Paul Bates, and Ehsan Forootan. "Comparing global hydrological models and combining them with GRACE by dynamic model data averaging (DMDA)." Advances in Water Resources 138 (April 2020): 103528. http://dx.doi.org/10.1016/j.advwatres.2020.103528.

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30

Drachal, Krzysztof. "Analysis of Agricultural Commodities Prices with New Bayesian Model Combination Schemes." Sustainability 11, no. 19 (2019): 5305. http://dx.doi.org/10.3390/su11195305.

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In the described research three agricultural commodities (i.e., wheat, corn and soybean) spot prices were analyzed. In particular, one-month ahead forecasts were built with techniques like dynamic model averaging (DMA), the median probability model and Bayesian model averaging. The common features of these methods are time-varying parameters approach toward estimation of regression coefficients and dealing with model uncertainty. In other words, starting with multiple potentially important explanatory variables, various component linear regression models can be constructed. Then, from these models an averaged forecast can be constructed. Moreover, the mentioned techniques can be easily modified from model averaging into a model selection approach. Considering as benchmark models, time-varying parameters regression with all considered potential price drivers, historical average, ARIMA (Auto-Regressive Integrated Moving Average) and the naïve forecast models, the Diebold–Mariano test suggested that DMA is an interesting alternative model, if forecast accuracy is the aim. Secondly, the interpretation of time-varying weights ascribed to component models containing a given variable suggested that economic development of emerging BRIC economies (Brazil, Russia, India and China) is recently one of the most important drivers of agricultural commodities prices. The analysis was made on the monthly data between 1976 and 2016. The initial price drivers were various fundamental, macroeconomic and financial factors.

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31

Cao, Wen Si, and Lu Hong Gong. "Simulation Analyses and Modeling Method of Time Averaging Principle-Based for Zero-Current-Switch Quasi Resonant Converters Boost Circuit." Applied Mechanics and Materials 65 (June 2011): 224–27. http://dx.doi.org/10.4028/www.scientific.net/amm.65.224.

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DC-DC switching converter is a time-variant and non-linear dynamic system, and it is difficult to analyze and design. The operating principles and four operating modes of Boost Zero-current-switch Quasi Resonant Converters (ZCS-QRCs) are analyzed. Its nonlinear model is built up based on time averaging Principle, illative process and the model built step are presented. The converter model is readily obtained by MATLAB, the waveforms of simulations of ZCS-QRCs Boost circuit models, mathematic models based on time averaging Principle of Quasi Resonant Converters are compared . At last, simulation results are verified and correspond to theory by comparing waveforms of simulations. Modeling Approach can be applicable other dc-dc Converter.

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32

Drachal, Krzysztof. "Some Novel Bayesian Model Combination Schemes: An Application to Commodities Prices." Sustainability 10, no. 8 (2018): 2801. http://dx.doi.org/10.3390/su10082801.

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Forecasting commodities prices on vividly changing markets is a hard problem to tackle. However, being able to determine important price predictors in a time-varying setting is crucial for sustainability initiatives. For example, the 2000s commodities boom gave rise to questioning whether commodities markets become over-financialized. In case of agricultural commodities, it was questioned if the speculative pressures increase food prices. Recently, some newly proposed Bayesian model combination scheme has been proposed, i.e., Dynamic Model Averaging (DMA). This method has already been applied with success in certain markets. It joins together uncertainty about the model and explanatory variables and a time-varying parameters approach. It can also capture structural breaks and respond to market disturbances. Secondly, it can deal with numerous explanatory variables in a data-rich environment. Similarly, like Bayesian Model Averaging (BMA), Dynamic Model Averaging (DMA), Dynamic Model Selection (DMS) and Median Probability Model (MED) start from Time-Varying Parameters’ (TVP) regressions. All of these methods were applied to 69 spot commodities prices. The period between Dec 1983 and Oct 2017 was analysed. In approximately 80% of cases, according to the Diebold–Mariano test, DMA produced statistically significant more accurate forecast than benchmark forecasts (like the naive method or ARIMA). Moreover, amongst all the considered model types, DMA was in 22% of cases the most accurate one (significantly). MED was most often minimising the forecast errors (28%). However, in the text, it is clarified that this was due to some specific initial parameters setting. The second ”best” model type was MED, meaning that, in the case of model selection, relying on the highest posterior probability is not always preferable.

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33

Drachal, Krzysztof. "Dynamic Model Averaging in Economics and Finance with fDMA: A Package for R." Signals 1, no. 1 (2020): 47–99. http://dx.doi.org/10.3390/signals1010004.

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The described R package allows to estimate Dynamic Model Averaging (DMA), Dynamic Model Selection (DMS) and Median Probability Model. The original methods, and additionally, some selected modifications of these methods are implemented. For example the user can choose between recursive moment estimation and exponentially moving average for variance updating in the base DMA. Moreover, inclusion probabilities can be computed in a way using “Google Trends” data. The code is written with respect to minimise the computational burden, which is quite an obstacle for DMA algorithm if numerous variables are used. For example, this package allows for parallel computations and implementation of the Occam’s window approach. However, clarity and readability of the code, and possibility for an R-familiar user to make his or her own small modifications in reasonably small time and with low effort are also taken under consideration. Except that, some alternative (benchmark) forecasts can also be quickly performed within this package. Indeed, this package is designed in a way that is hoped to be especially useful for practitioners and researchers in economics and finance.

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34

Marfatia, Hardik A. "Forecasting Interconnections in International Housing Markets: Evidence from the Dynamic Model Averaging Approach." Journal of Real Estate Research 42, no. 1 (2020): 37–103. http://dx.doi.org/10.22300/0896-5803.42.1.37.

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In this paper, I undertake a novel approach to uncover the forecasting interconnections in the international housing markets. Using a dynamic model averaging framework that allows both the coefficients and the entire forecasting model to dynamically change over time, I uncover the intertwined forecasting relationships in 23 leading international housing markets. The evidence suggests significant forecasting interconnections in these markets. However, no country holds a constant forecasting advantage, including the United States and the United Kingdom, although the U.S. housing market's predictive power has increased over time. Evidence also suggests that allowing the forecasting model to change is more important than allowing the coefficients to change over time.

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35

Xu, Siqi, Yifeng Zhang, and Xiaodan Chen. "Forecasting Carbon Emissions with Dynamic Model Averaging Approach: Time-Varying Evidence from China." Discrete Dynamics in Nature and Society 2020 (October 26, 2020): 1–14. http://dx.doi.org/10.1155/2020/8827440.

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Although energy-related factors, such as energy intensity and energy consumption, are well recognized as major drivers of carbon dioxide emission in China, little is known about the time-varying impacts of other macrolevel nonenergy factors on carbon emission, especially those from macroeconomic, financial, household, and technology progress indicators in China. This paper contributes to the literature by investigating the time-varying predictive ability of 15 macrolevel indicators for China’s carbon dioxide emission from 1982 to 2017 with a dynamic model averaging (DMA) method. The empirical results show that, firstly, the explanatory power of each nonenergy predictor changes significantly with time and no predictor has a stable positive/negative impact on China’s carbon emissions throughout the whole sample period. Secondly, all these predictors present a distinct predictive ability for carbon emission in China. The proportion of industry production in GDP (IP) shows the greatest predictive power, while the proportion of FDI in GDP has the smallest forecasting ability. Interestingly, those Chinese household features, such as Engel’s coefficient and household savings rate, play very important roles in the prediction of China’s carbon emission. In addition, we find that IP are losing its predictive power in recent years, while the proportion of value-added of the service sector in GDP presents not only a leading forecasting weight, but a continuous increasing prediction power in recent years. Finally, the dynamic model averaging (DMA) method can produce the most accurate forecasts of carbon emission in China compared to other commonly used forecasting methods.

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36

Nonejad, Nima. "Predicting equity premium using dynamic model averaging. Does the state–space representation matter?" North American Journal of Economics and Finance 57 (July 2021): 101442. http://dx.doi.org/10.1016/j.najef.2021.101442.

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37

Agrawal, Shaashwat, Aditi Chowdhuri, Sagnik Sarkar, Ramani Selvanambi, and Thippa Reddy Gadekallu. "Temporal Weighted Averaging for Asynchronous Federated Intrusion Detection Systems." Computational Intelligence and Neuroscience 2021 (December 17, 2021): 1–10. http://dx.doi.org/10.1155/2021/5844728.

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Federated learning (FL) is an emerging subdomain of machine learning (ML) in a distributed and heterogeneous setup. It provides efficient training architecture, sufficient data, and privacy-preserving communication for boosting the performance and feasibility of ML algorithms. In this environment, the resultant global model produced by averaging various trained client models is vital. During each round of FL, model parameters are transferred from each client device to the server while the server waits for all models before it can average them. In a realistic scenario, waiting for all clients to communicate their model parameters, where client models are trained on low-power Internet of Things (IoT) devices, can result in a deadlock. In this paper, a novel temporal model averaging algorithm is proposed for asynchronous federated learning (AFL). Our approach uses a dynamic expectation function that computes the number of client models expected in each round and a weighted averaging algorithm for continuous modification of the global model. This ensures that the federated architecture is not stuck in a deadlock all the while increasing the throughput of the server and clients. To implicate the importance of asynchronicity in cybersecurity, the proposed algorithm is tested using NSL-KDD intrusion detection system datasets. The performance accuracy of the global model is about 99.5% on the dataset, outperforming traditional FL models in anomaly detection. In terms of asynchronicity, we get an increased throughput of almost 10.17% for every 30 timesteps.

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38

Chen, Huigang, Alin Mirestean, and Charalambos Tsangarides. "Limited Information Bayesian Model Averaging for Dynamic Panels with an Application to a Trade Gravity Model." IMF Working Papers 11, no. 230 (2011): 1. http://dx.doi.org/10.5089/9781463921309.001.

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39

Fang, Angbo. "Dynamical effective field model for interacting ferrofluids: II. The proper relaxation time and effects of dynamic correlations." Journal of Physics: Condensed Matter 34, no. 11 (2021): 115103. http://dx.doi.org/10.1088/1361-648x/ac4346.

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Abstract The recently proposed dynamical effective field model (DEFM) is quantitatively accurate for ferrofluid dynamics. It is derived in paper I within the framework of dynamical density functional theory (DDFT) along with a phenomenological description of nonadiabatic effects. However, it remains to clarify how the characteristic rotational relaxation time of a dressed particle, denoted by τ r , is quantitatively related to that of a bare particle, denoted by τ r 0 . By building macro-micro connections via two different routes, I reveal that under some gentle assumptions τ r can be identified with the mean time characterizing long-time rotational self-diffusion. I further introduce two simple but useful integrated correlation factors, describing the effects of quasi-static (adiabatic) and dynamic (nonadiabatic) inter-particle correlations, respectively. In terms of both the dynamic magnetic susceptibility is expressed in an illuminating and elegant form. Remarkably, it shows that the macro-micro connection is established via two successive steps: a dynamical coarse-graining with nonadiabatic effects accounted for by the dynamic factor, followed by equilibrium ensemble averaging captured by the static factor. By analyzing data from Brownian dynamics simulations on monodisperse interacting ferrofluids, I find τ r / τ r 0 is, somehow unexpectedly, insensitive to changes of particle volume fraction. A physical picture is proposed to explain it. Furthermore, an empirical formula is proposed to characterize the dependence of τ r / τ r 0 on dipole-dipole interaction strength. The DEFM supplemented with this formula leads to parameter-free predictions in good agreement with results from Brownian dynamics simulations. The theoretical developments presented in this paper may have important consequences to studies of ferrofluid dynamics in particular and other systems modeled by DDFTs in general.

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40

Kliber, Paweł. "DETERMINANTS OF THE SPREAD BETWEEN POLONIA RATE AND THE REFERENCE RATE – DYNAMIC MODEL AVERAGING APPROACH." Prace Naukowe Uniwersytetu Ekonomicznego we Wrocławiu, no. 482 (2017): 107–20. http://dx.doi.org/10.15611/pn.2017.482.09.

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41

Sotiropoulos, Dimitrios A. "Dynamic Stiffness of Cracked Interfaces." Journal of Applied Mechanics 57, no. 2 (1990): 476–78. http://dx.doi.org/10.1115/1.2892017.

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Quantitative relationships are derived between the dynamic macromechanical stiffness and microparameters of planar interfaces containing distributed cracks. The derivation is based on the solution of the problem of elastic wave reflection by a plane with a continuous distribution of springs to model the cracked interface at the macrolevel. The dynamic spring stiffness is then, through averaging, related to crack-opening volumes and other microparameters. For linear springs and periodic crack distributions, numerical examples are presented for plain strain. The stiffness is shown to strongly depend on frequency.

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42

Farhadi, Akram, Joshua Chern, Daniel Hirsh, et al. "Intracranial Pressure Forecasting in Children Using Dynamic Averaging of Time Series Data." Forecasting 1, no. 1 (2018): 47–58. http://dx.doi.org/10.3390/forecast1010004.

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Increased Intracranial Pressure (ICP) is a serious and often life-threatening condition. If the increased pressure pushes on critical brain structures and blood vessels, it can lead to serious permanent problems or even death. In this study, we propose a novel regression model to forecast ICP episodes in children, 30 min in advance, by using the dynamic characteristics of continuous intracranial pressure, vitals and medications during the last two hours. The correlation between physiological parameters, including blood pressure, respiratory rate, heart rate and the ICP, is analyzed. Linear regression, Lasso regression, support vector machine and random forest algorithms are used to forecast the next 30 min of the recorded ICP. Finally, dynamic features are created based on vitals, medications and the ICP. The weak correlation between blood pressure and the ICP (0.2) is reported. The Root-Mean-Square Error (RMSE) of the random forest model decreased from 1.6 to 0.89% by using the given medication variables in the last two hours. The random forest regression gave an accurate model for the ICP forecast with 0.99 correlation between the forecast and experimental values.

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43

Ma, You Jie, Si Jia Liu, Xue Song Zhou, Cheng Wen Tian, and Fang Liang. "Modeling and Simulation for Boost Converter Based on HDS Theory." Advanced Materials Research 383-390 (November 2011): 2313–17. http://dx.doi.org/10.4028/www.scientific.net/amr.383-390.2313.

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As a typical power electronic circuit, the dynamic model of boost converter can be viewed as a mixing or interacting system between the discrete event and continuous time variable, also known as hybrid dynamic systems. By using of hybrid dynamic systems theory, a hybrid model of boost converter was established and simulation was carried out to verify the validity of the method in the MATLAB. Compared with traditional state-space averaging method, a more accurate model of boost converter was obtained without treatment of approximate linearization based on hybrid dynamic theory.

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44

Qiu, Chen, Stephan Mandt, and Maja Rudolph. "History Marginalization Improves Forecasting in Variational Recurrent Neural Networks." Entropy 23, no. 12 (2021): 1563. http://dx.doi.org/10.3390/e23121563.

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Deep probabilistic time series forecasting models have become an integral part of machine learning. While several powerful generative models have been proposed, we provide evidence that their associated inference models are oftentimes too limited and cause the generative model to predict mode-averaged dynamics. Mode-averaging is problematic since many real-world sequences are highly multi-modal, and their averaged dynamics are unphysical (e.g., predicted taxi trajectories might run through buildings on the street map). To better capture multi-modality, we develop variational dynamic mixtures (VDM): a new variational family to infer sequential latent variables. The VDM approximate posterior at each time step is a mixture density network, whose parameters come from propagating multiple samples through a recurrent architecture. This results in an expressive multi-modal posterior approximation. In an empirical study, we show that VDM outperforms competing approaches on highly multi-modal datasets from different domains.

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Drachal, Krzysztof. "Determining Time-Varying Drivers of Spot Oil Price in a Dynamic Model Averaging Framework." Energies 11, no. 5 (2018): 1207. http://dx.doi.org/10.3390/en11051207.

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Schepen, Andrew, and Q. J. Wang. "Model averaging methods to merge operational statistical and dynamic seasonal streamflow forecasts in Australia." Water Resources Research 51, no. 3 (2015): 1797–812. http://dx.doi.org/10.1002/2014wr016163.

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Hou, Dian Li, and Qing Fan Zhang. "A Modeling and Analysis of Push-Pull Forward Topology." Applied Mechanics and Materials 40-41 (November 2010): 293–97. http://dx.doi.org/10.4028/www.scientific.net/amm.40-41.293.

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By circuit averaging method, a small-signal model is derived from push-pull forward topology which works in Continuous Conduction Mode (CCM). Dynamic large-signal model, DC circuit model and small-signal model are derived. The effect of leakage inductance on push-pull forward topology is analyzed and simulated in detail.

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Chen, Zunming, Hongyan Cui, Ensen Wu, and Xi Yu. "Dynamic Asynchronous Anti Poisoning Federated Deep Learning with Blockchain-Based Reputation-Aware Solutions." Sensors 22, no. 2 (2022): 684. http://dx.doi.org/10.3390/s22020684.

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As promising privacy-preserving machine learning technology, federated learning enables multiple clients to train the joint global model via sharing model parameters. However, inefficiency and vulnerability to poisoning attacks significantly reduce federated learning performance. To solve the aforementioned issues, we propose a dynamic asynchronous anti poisoning federated deep learning framework to pursue both efficiency and security. This paper proposes a lightweight dynamic asynchronous algorithm considering the averaging frequency control and parameter selection for federated learning to speed up model averaging and improve efficiency, which enables federated learning to adaptively remove the stragglers with low computing power, bad channel conditions, or anomalous parameters. In addition, a novel local reliability mutual evaluation mechanism is presented to enhance the security of poisoning attacks, which enables federated learning to detect the anomalous parameter of poisoning attacks and adjust the weight proportion of in model aggregation based on evaluation score. The experiment results on three datasets illustrate that our design can reduce the training time by 30% and is robust to the representative poisoning attacks significantly, confirming the applicability of our scheme.

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YUAN, PING. "FORECASTING REALIZED VOLATILITY DYNAMICALLY BASED ON ADJUSTED DYNAMIC MODEL AVERAGING (AMDA) APPROACH: EVIDENCE FROM CHINA’S STOCK MARKET." Annals of Financial Economics 14, no. 04 (2019): 1950022. http://dx.doi.org/10.1142/s2010495219500222.

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In this study, we forecast the realized volatility of the CSI 300 index using the heterogeneous autoregressive model for realized volatility (HAR-RV) and its various extensions. Our models take into account the time-varying property of the models’ parameters and the volatility of realized volatility. The adjusted dynamic model averaging (ADMA) approach, is used to combine the forecasts of the individual models. Our empirical results suggest that ADMA can generate more accurate forecasts than DMA method and alternative strategies. Models that use time-varying parameters have greater forecasting accuracy than models that use the constant coefficients.

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Rizopoulos, Dimitris, Laura A. Hatfield, Bradley P. Carlin, and Johanna J. M. Takkenberg. "Combining Dynamic Predictions From Joint Models for Longitudinal and Time-to-Event Data Using Bayesian Model Averaging." Journal of the American Statistical Association 109, no. 508 (2014): 1385–97. http://dx.doi.org/10.1080/01621459.2014.931236.

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