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Literatura académica sobre el tema "Periodic prediction"

Autor: Grafiati

Publicado: 1 de junio de 2024

Última modificación: 31 de julio de 2025

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Artículos de revistas sobre el tema "Periodic prediction"

1

Niu, Xiaoxu, Junwei Ma, Yankun Wang, Junrong Zhang, Hongjie Chen, and Huiming Tang. "A Novel Decomposition-Ensemble Learning Model Based on Ensemble Empirical Mode Decomposition and Recurrent Neural Network for Landslide Displacement Prediction." Applied Sciences 11, no. 10 (2021): 4684. http://dx.doi.org/10.3390/app11104684.

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As vital comments on landslide early warning systems, accurate and reliable displacement prediction is essential and of significant importance for landslide mitigation. However, obtaining the desired prediction accuracy remains highly difficult and challenging due to the complex nonlinear characteristics of landslide monitoring data. Based on the principle of “decomposition and ensemble”, a three-step decomposition-ensemble learning model integrating ensemble empirical mode decomposition (EEMD) and a recurrent neural network (RNN) was proposed for landslide displacement prediction. EEMD and ku

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2

Yang, Xiaoxue, Yajie Zou, Jinjun Tang, Jian Liang, and Muhammad Ijaz. "Evaluation of Short-Term Freeway Speed Prediction Based on Periodic Analysis Using Statistical Models and Machine Learning Models." Journal of Advanced Transportation 2020 (January 20, 2020): 1–16. http://dx.doi.org/10.1155/2020/9628957.

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Accurate prediction of traffic information (i.e., traffic flow, travel time, traffic speed, etc.) is a key component of Intelligent Transportation System (ITS). Traffic speed is an important indicator to evaluate traffic efficiency. Up to date, although a few studies have considered the periodic feature in traffic prediction, very few studies comprehensively evaluate the impact of periodic component on statistical and machine learning prediction models. This paper selects several representative statistical models and machine learning models to analyze the influence of periodic component on sho

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3

Ren, Liang, Feng Yang, Yuanhe Gao, and Yongcong He. "Predicting Spacecraft Telemetry Data by Using Grey–Markov Model with Sliding Window and Particle Swarm Optimization." Journal of Mathematics 2023 (February 3, 2023): 1–14. http://dx.doi.org/10.1155/2023/9693047.

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Predicting telemetry data is vital for the proper operation of orbiting spacecraft. The Grey–Markov model with sliding window (GMSW) combines Grey model (GM (1, 1)) and Markov chain forecast model, which allows it to describe the fluctuation of telemetry data. However, the Grey–Markov model with sliding window does not provide better predictions of telemetry series with the pseudo-periodic phenomenon. To overcome this drawback, we improved the GMSW model by applying particle swarm optimization (PSO) algorithm a sliding window for better prediction of spacecraft telemetry data (denoted as PGMSW

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4

Lin, Hui-Ting Christine, and Vincent S. Tseng. "Periodic Transformer Encoder for Multi-Horizon Travel Time Prediction." Electronics 13, no. 11 (2024): 2094. http://dx.doi.org/10.3390/electronics13112094.

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In the domain of Intelligent Transportation Systems (ITS), ensuring reliable travel time predictions is crucial for enhancing the efficiency of transportation management systems and supporting long-term planning. Recent advancements in deep learning have demonstrated the ability to effectively leverage large datasets for accurate travel time predictions. These innovations are particularly vital as they address both short-term and long-term travel demands, which are essential for effective traffic management and scheduled routing planning. Despite advances in deep learning applications for traf

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5

Sugimoto, Masashi, Naoya Iwamoto, Robert W. Johnston, Keizo Kanazawa, Yukinori Misaki, and Kentarou Kurashige. "A Study of Predicting Ability in State-Action Pair Prediction." International Journal of Artificial Life Research 7, no. 1 (2017): 52–66. http://dx.doi.org/10.4018/ijalr.2017010104.

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When a robot considers an action-decision based on a future prediction, it is necessary to know the property of disturbance signals from the outside environment. On the other hand, the properties of disturbance signals cannot be described simply, such as non-periodic function, nonlinear time-varying function nor almost-periodic function. In case of a robot control, sampling rate for control will be affected description of disturbance signals such as frequency or amplitude. If the sampling rate for acquiring a disturbance signal is not correct, the action will be taken far from its actual prope

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6

Shen, Yueqian, Xiaoxia Ma, Yajing Sun, and Sheng Du. "Prediction of university fund revenue and expenditure based on fuzzy time series with a periodic factor." PLOS ONE 18, no. 5 (2023): e0286325. http://dx.doi.org/10.1371/journal.pone.0286325.

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Financial management and decision-making of universities play an essential role in their development. Predicting fund revenue and expenditure of universities can provide a necessary basis for funds risk prevention. For the lack of solid data reference for financial management and funds risk prevention in colleges and universities, this paper presents a prediction model of University fund revenue and expenditure based on fuzzy time series with a periodic factor. Combined with the fuzzy time series, this prediction method introduces the periodic factor of university funds. The periodic factor is

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7

Cheng, Weiwei, Guigen Nie, and Jian Zhu. "Characterizing Periodic Variations of Atomic Frequency Standards via Their Frequency Stability Estimates." Sensors 23, no. 11 (2023): 5356. http://dx.doi.org/10.3390/s23115356.

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The onboard atomic frequency standard (AFS) is a crucial element of Global Navigation Satellite System (GNSS) satellites. However, it is widely accepted that periodic variations can influence the onboard AFS. The presence of non-stationary random processes in AFS signals can lead to inaccurate separation of the periodic and stochastic components of satellite AFS clock data when using least squares and Fourier transform methods. In this paper, we characterize the periodic variations of AFS using Allan and Hadamard variances and demonstrate that the Allan and Hadamard variances of the periodics

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8

Miao, Xu, Bing Wu, Yajie Zou, and Lingtao Wu. "Examining the Impact of Different Periodic Functions on Short-Term Freeway Travel Time Prediction Approaches." Journal of Advanced Transportation 2020 (August 1, 2020): 1–15. http://dx.doi.org/10.1155/2020/3463287.

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Freeway travel time prediction is a key technology of Intelligent Transportation Systems (ITS). Many scholars have found that periodic function plays a positive role in improving the prediction accuracy of travel time prediction models. However, very few studies have comprehensively evaluated the impacts of different periodic functions on statistical and machine learning models. In this paper, our primary objective is to evaluate the performance of the six commonly used multistep ahead travel time prediction models (three statistical models and three machine learning models). In addition, we c

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Scerri, Eric R., and John Worrall. "Prediction and the periodic table." Studies in History and Philosophy of Science Part A 32, no. 3 (2001): 407–52. http://dx.doi.org/10.1016/s0039-3681(01)00023-1.

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Pawelzik, K., and H. G. Schuster. "Unstable periodic orbits and prediction." Physical Review A 43, no. 4 (1991): 1808–12. http://dx.doi.org/10.1103/physreva.43.1808.

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Tesis sobre el tema "Periodic prediction"

1

Chen, Jin-Jae. "Prediction of periodic forced response of frictionally constrained turbine blades /." The Ohio State University, 1999. http://rave.ohiolink.edu/etdc/view?acc_num=osu1488187763847997.

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Sadat, Hosseini Seyed Hamid Stern Frederick Carrica Pablo M. "CFD prediction of ship capsize parametric rolling, broaching, surf-riding, and periodic motions /." [Iowa City, Iowa] : University of Iowa, 2009. http://ir.uiowa.edu/etd/427.

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Date, James Charles. "Performance prediction of high lift rudders operating under steady and periodic flow conditions." Thesis, University of Southampton, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.390722.

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Sadat, Hosseini Seyed Hamid. "CFD prediction of ship capsize: parametric rolling, broaching, surf-riding, and periodic motions." Diss., University of Iowa, 2009. https://ir.uiowa.edu/etd/427.

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Stability against capsizing is one of the most fundamental requirements to design a ship. In this research, for the first time, CFD is performed to predict main modes of capsizing. CFD first is conducted to predict parametric rolling for a naval ship. Then CFD study of parametric rolling is extended for prediction of broaching both by using CFD as input to NDA model of broaching in replacement of EFD inputs or by using CFD for complete simulation of broaching. The CFD resistance, static heel and drift in calm water and static heel in following wave simulations are conducted to estimate inputs

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Perreira, Das Chagas Thiago. "Stabilization of periodic orbits in discrete and continuous-time systems." Phd thesis, Université Paris Sud - Paris XI, 2013. http://tel.archives-ouvertes.fr/tel-00852424.

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The main problem evaluated in this manuscript is the stabilization of periodic orbits of non-linear dynamical systems by use of feedback control. The goal of the control methods proposed in this work is to achieve a stable periodic oscillation. These control methods are applied to systems that present unstable periodic orbits in the state space, and the latter are the orbits to be stabilized.The methods proposed here are such that the resulting stable oscillation is obtained with low control effort, and the control signal is designed to converge to zero when the trajectory tends to the stabili

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6

Lindsey, Justin. "Fatigue Behavior in the Presence of Periodic Overloads Including the Effects of Mean Stress and Inclusions." University of Toledo / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1319554971.

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Borda, Jorge Victor Quiñones. "Log periodic analysis of critical crashes in the portuguese stock market." Master's thesis, Instituto Superior de Economia e Gestão, 2015. http://hdl.handle.net/10400.5/11082.

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Mestrado em Ciências Empresariais<br>O estudo de fenómenos críticos que se originaram nas ciências naturais e encontraram muitos campos de aplicação foi estendido nos últimos anos aos campos da economia de finanças, fornecendo aos investigadores novas abordagens para problemas conhecidos, nomeadamente aos que estão relacionados com a gestão de risco, a previsão, o estudo de bolhas financeiras e crashes, e muitos outros tipos de problemas que envolvem sistemas com criticalidade auto-organizada. A teoria de singularidades de tempo oscilatório auto-similares é apresentada, uma metodologia práti

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Devarasetty, Ravi Kiran. "Heuristic Algorithms for Adaptive Resource Management of Periodic Tasks in Soft Real-Time Distributed Systems." Thesis, Virginia Tech, 2001. http://hdl.handle.net/10919/31219.

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Dynamic real-time distributed systems are characterized by significant run-time uncertainties at the mission and system levels. Typically, processing and communication latencies in such systems do not have known upper bounds and event and task arrivals and failure occurrences are non-deterministically distributed. This thesis proposes adaptive resource management heuristic techniques for periodic tasks in dynamic real-time distributed systems with the (soft real-time) objective of minimizing missed deadline ratios. The proposed resource management techniques continuously monitor the applicatio

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Kamisetty, Jananni Narasimha Shiva Sai Sri Harsha Vardhan. "Forecasting Trajectory Data : A study by Experimentation." Thesis, Blekinge Tekniska Högskola, Institutionen för kommunikationssystem, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-13976.

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Context. The advances in location-acquisition and mobile computing techniques have generated massive spatial trajectory data. Such spatial trajectory data accumulated by telecommunication operators is huge, analyzing the data with a right tool or method can uncover patterns and connections which can be used for improving telecom services. Forecasting trajectory data or predicting next location of users is one of such analysis. It can be used for producing synthetic data and also to determine the network capacity needed for a cell tower in future. Objectives. The objectives of this thesis is, F

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Zhou, Shuai. "Improvement of the prediction of the ductility limits of polycrystalline materials by using relevant multiscale schemes." Electronic Thesis or Diss., Paris, ENSAM, 2025. http://www.theses.fr/2025ENAME008.

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L’objectif principal de cette thèse de doctorat est d’améliorer les capacités des méthodes et outils numériques développés dans la prédiction des limites de ductilité des tôles métalliques polycristallines à l’aide de la méthode des éléments finis en plasticité cristalline (CPFEM). Cette méthode utilise un Volume Elémentaire Représentatif (VER) polycristallin pour capter avec précision les caractéristiques mécaniques des tôles étudiées. La technique d’homogénéisation périodique est adoptée pour assurer la transition entre l’échelle du VER et celle du monocristal. À l’échelle du monocristal, le

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Libros sobre el tema "Periodic prediction"

1

Dolph, K. Leroy. Prediction of periodic basal area increment for young-growth mixed conifers in the Sierra Nevada. U.S. Dept. of Agriculture, Forest Service, Pacific Southwest Forest and Range Experiment Station, 1988.

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2

BEREZhNOY, Aleksandr, Svetlana DUNAEVSKAYa, and Yuriy VINNIK. Prognosis of postoperative course of urolithiasis. INFRA-M Academic Publishing LLC., 2022. http://dx.doi.org/10.12737/1863093.

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The monograph devoted to the study of urolithiasis consistently highlights the issues of etiology, classification, diagnosis and modern principles of treatment of urolithiasis. The problems of postoperative complications in surgery and urology are considered as a separate issue, data on original methods for predicting the development of hemorrhagic or inflammatory complications in the postoperative period with urolithiasis are presented. Special attention is paid to the issues of nonspecific immune protection, immune status indicators and hemostasis system in the development of complications i

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3

Terehin, Valeriy, and Viktor Chernyshov. Efficiency and effectiveness of the penitentiary system: assessment and planning. INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1079434.

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The issues of setting goals, planning and forming a system of indicators of the effectiveness and efficiency of the penal system are considered. The criteria for determining the goals-tasks that are adequate to the public goals of the system are justified. Quantitative indicators corresponding to the criteria were developed, based on the contribution of the criminal justice System to reducing the socio-economic losses of society from recidivism. The contribution of the system is determined by changes in the criminal potential of convicted persons during the period of serving a sentence under a

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4

Wang, Bin. Intraseasonal Modulation of the Indian Summer Monsoon. Oxford University Press, 2018. http://dx.doi.org/10.1093/acrefore/9780190228620.013.616.

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The strongest Indian summer monsoon (ISM) on the planet features prolonged clustered spells of wet and dry conditions often lasting for two to three weeks, known as active and break monsoons. The active and break monsoons are attributed to a quasi-periodic intraseasonal oscillation (ISO), which is an extremely important form of the ISM variability bridging weather and climate variation. The ISO over India is part of the ISO in global tropics. The latter is one of the most important meteorological phenomena discovered during the 20th century (Madden & Julian, 1971, 1972). The extreme dry an

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5

Kalitzin, Stiliyan, and Fernando Lopes da Silva. EEG-Based Anticipation and Control of Seizures. Edited by Donald L. Schomer and Fernando H. Lopes da Silva. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228484.003.0023.

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Early seizure-prediction paradigms were based on detecting electroencephalographic (EEG) features, but recent approaches are based on dynamic systems theory. Methods that attempted to detect predictive features during the preictal period proved difficult to validate in practice. Brain systems can display bistability (both normal and epileptic states can coexist), and the transitions between states may be initiated by external or internal dynamic factors. In the former case prediction is impossible, but in the latter case prediction is conceivable, leading to the hypothesis that as seizure onse

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6

ANTONIO. Periodic Tables Unifying Living Organi : Periodic Tables Unifying Living Organisms at the Molecular Level: The Predictive Power of the Law Of. World Scientific Publishing Co Pte Ltd, 2018.

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National Aeronautics and Space Administration (NASA) Staff. Predictions of Control Inputs, Periodic Responses and Damping Levels of an Isolated Experimental Rotor in Trimmed Flight. Independently Published, 2018.

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Periodic Tables Unifying Living Organisms at the Molecular Level: The Predictive Power of the Law of Periodicity. World Scientific Publishing Co Pte Ltd, 2018.

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9

Barbaree, Howard E., and Robert A. Prentky. Risk assessment of sex offenders. Edited by Teela Sanders. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780190213633.013.21.

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This essay discusses the assessment of recidivism risk in sex offenders. It begins with definitions of critical terms and concepts. A number of approaches to risk assessment are described. Validated risk instruments are reviewed, with a focus on their reliability and accuracy in predicting recidivism. Actuarial assessment of risk is described as a two-stage process. In the first stage, offenders are assessed and assigned to a risk level or stratum. In the second stage, the probability of risk over a follow-up period is estimated based on the offender’s risk ranking. The essay discusses calibra

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Dawid, A. Philip, Julia Mortera, and Paola Vicard. Volatility in prediction markets: A measure of information flow in political campaigns. Edited by Anthony O'Hagan and Mike West. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198703174.013.21.

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This article discusses the use of Bayesian analysis in the evaluation of temporal volatility and information flows in political campaigns. Using the 2004 US presidential election campaign as a case study, it demonstrates the utility of a model with two volatility regimes that simplifies the task of associating events with periods of high information. The article first explains why prediction markets are able to aggregate information such that the prices of future contracts are reflective of the event’s actual probability of occurring before analysing data from futures on ‘Bush wins the popular

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Capítulos de libros sobre el tema "Periodic prediction"

1

McCormick, Andrew C., and Asoke K. Nandi. "Condition Monitoring Using Periodic Time-Varying AR Models." In Signal Analysis and Prediction. Birkhäuser Boston, 1998. http://dx.doi.org/10.1007/978-1-4612-1768-8_14.

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Bračič, Maja, and Aneta Stefanovska. "Lyapunov Exponents of Simulated and Measured Quasi-Periodic Flows." In Signal Analysis and Prediction. Birkhäuser Boston, 1998. http://dx.doi.org/10.1007/978-1-4612-1768-8_34.

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Burgess, Keith, Katie Burgess, Prajan Subedi, Phil Ainslie, Zbigniew Topor, and William Whitelaw. "Prediction of Periodic Breathing at Altitude." In Integration in Respiratory Control. Springer New York, 2008. http://dx.doi.org/10.1007/978-0-387-73693-8_77.

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Michalak, Marcin. "Time Series Prediction with Periodic Kernels." In Computer Recognition Systems 4. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20320-6_15.

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Blatov, Vladislav A., and Davide M. Proserpio. "Periodic-Graph Approaches in Crystal Structure Prediction." In Modern Methods of Crystal Structure Prediction. Wiley-VCH Verlag GmbH & Co. KGaA, 2010. http://dx.doi.org/10.1002/9783527632831.ch1.

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Cao, Yongzhong, He Zhou, and Bin Li. "Rice Growth Prediction Based on Periodic Growth." In Studies in Computational Intelligence. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-56178-9_13.

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Zhao, Jijun, Hao Liu, Zhihua Li, and Wei Li. "Periodic Data Prediction Algorithm in Wireless Sensor Networks." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-36252-1_65.

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Bittanti, Sergio. "The periodic prediction problem for cyclostationary processes — an introduction." In Modelling, Robustness and Sensitivity Reduction in Control Systems. Springer Berlin Heidelberg, 1987. http://dx.doi.org/10.1007/978-3-642-87516-8_15.

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Karimi, M., P. Croaker, and N. Kessissoglou. "Trailing-Edge Noise Prediction Using a Periodic BEM Technique." In Fluid-Structure-Sound Interactions and Control. Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-48868-3_6.

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Bouzayane, Sarra, and Ines Saad. "Intelligent Multicriteria Decision Support System for a Periodic Prediction." In Decision Support Systems IX: Main Developments and Future Trends. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-18819-1_8.

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Actas de conferencias sobre el tema "Periodic prediction"

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Krishnamurthy, Siddhartha, Aric Aumann, Stephen Rizzi, and D. Douglas. "Auralization of Rotorcraft Periodic Flyover Noise from Design Predictions." In Vertical Flight Society 74th Annual Forum & Technology Display. The Vertical Flight Society, 2018. http://dx.doi.org/10.4050/f-0074-2018-12664.

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This paper combines the synthesis and propagation of rotorcraft sounds, or auralizations, with their noise predictions and design optimization to incorporate human response into the process of creating low-noise rotorcraft. The NASA Auralization Framework is described and used to auralize the sounds of an AS350 helicopter main rotor at a ground observer from predicted acoustic pressure time histories. This paper uses spherical phase interpolation as part of the sound synthesis. This interpolation is an improvement over the linear phase interpolation described in previous work, which can introd

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Yuan, Zhongju, Geraint Wiggins, and Dick Botteldooren. "A novel Reservoir Architecture for Periodic Time Series Prediction." In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10650592.

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Lin, Hui-Ting, Hao Dai, and Vincent S. Tseng. "Periodic Stacked Transformer-based Framework for Travel Time Prediction." In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10650659.

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Yu, Yongfu, Xingsheng Xie, and Xing Ju. "DPGARN: Dynamic Periodic Graph Attention Recurrent Network for Traffic Flow Prediction." In 2024 43rd Chinese Control Conference (CCC). IEEE, 2024. http://dx.doi.org/10.23919/ccc63176.2024.10662753.

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Li, Ruize, Shuai Wang, Baoshen Guo, Shuai Wang, Xiaolei Zhou, and Wei Xi. "O2O Logistics Customer Value Prediction with Periodic Asynchronous Vertical Federated Learning." In 2024 IEEE International Conference on High Performance Computing and Communications (HPCC). IEEE, 2024. https://doi.org/10.1109/hpcc64274.2024.00077.

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Wang, Mingxin, Xinlei Chen, Fan Gao, Hao Meng, Wu Yang, and Weibing Lu. "Rapid Prediction of EM Scattering from Finite Periodic Structures Using CBFM with ANNs." In 2024 IEEE 12th Asia-Pacific Conference on Antennas and Propagation (APCAP). IEEE, 2024. https://doi.org/10.1109/apcap62011.2024.10880996.

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Gillan, Mark, R. Mitchell, S. Raghunathan, et al. "Prediction and control of periodic flows." In 35th Aerospace Sciences Meeting and Exhibit. American Institute of Aeronautics and Astronautics, 1997. http://dx.doi.org/10.2514/6.1997-832.

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Sun Bo, Zhang Bingyi, Wang Erzhi, and Sun Liang. "Periodic statistical prediction adaptive memory incremental control." In 2008 IEEE International Conference on Industrial Technology - (ICIT). IEEE, 2008. http://dx.doi.org/10.1109/icit.2008.4608382.

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Hu, Xiaobo, and Gang Quan. "Fast performance prediction for periodic task systems." In the eighth international workshop. ACM Press, 2000. http://dx.doi.org/10.1145/334012.334026.

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Nakhjiri, Mehdi, and Peter F. Pelz. "Turbomachines Under Periodic Admission: Axiomatic Performance Prediction." In ASME Turbo Expo 2012: Turbine Technical Conference and Exposition. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/gt2012-68398.

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In calibration tasks under engine conditions, steady-state manufacturer performance maps are applied to periodic turbocharger operation. This procedure is exposed to considerable uncertainties. In this work the axiomatic form of the energy equation as well as Euler turbomachinery equation are used to generate a general form of the respective equations which allow for periodicity. Thus, the concept of apparent speed and apparent efficiency is introduced. The latter can attain values greater than unity.

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Informes sobre el tema "Periodic prediction"

1

Dolph, Leroy K. Prediction of periodic basal area increment for young-growth mixed conifers in sierra Nevada. U.S. Department of Agriculture, Forest Service, Pacific Southwest Forest and Range Experiment Station, 1988. http://dx.doi.org/10.2737/psw-rp-190.

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Leis. L51865 Hydrotest Parameters to Help Control High-pH SCC on Gas Transmission Pipelines. Pipeline Research Council International, Inc. (PRCI), 1999. http://dx.doi.org/10.55274/r0010208.

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This report develops and validates a probabilistic model for high pH SCC on pipelines that can help operators to: quantify the condition of susceptible transmission pipelines assess the efficiency of controls for such SCC based on after coolers and periodic hydrostatic re-testing identify, evaluate, and control the related risks· prioritize rehabilitation to avoid in-service incidents. The probabilistic model is an adaptation of a first-generation model for high pH SCC known as the Stress-Corrosion Cracking Life Prediction Model, (SCCLPM). Its validation follows from the close match between si

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Gómez Loscos, Ana, Miguel Ángel González Simón, and Matías José Pacce. Short-term real-time forecasting model for spanish GDP (Spain-STING): new specification and reassessment of its predictive power. Banco de España, 2024. http://dx.doi.org/10.53479/36137.

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The predictive power of short-term forecasting models was impaired by the increased volatility observed in most economic indicators following the outbreak of COVID-19. This paper sets out a revision of the Spain-STING model (one of the tools used by the Banco de España for short-term forecasts of quarter-on-quarter GDP growth) with a view to improving its predictive power in the wake of the pandemic. In particular, the revision entails three main changes: (i) the correlation between the indicators included in the model and the estimated common component is now coincident for all of the indicat

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Соловйов, В. М., та В. В. Соловйова. Моделювання мультиплексних мереж. Видавець Ткачук О.В., 2016. http://dx.doi.org/10.31812/0564/1253.

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From the standpoint of interdisciplinary self-organization theories and synergetics analyzes current approaches to modeling socio-economic systems. It is shown that the complex network paradigm is the foundation on which to build predictive models of complex systems. We consider two algorithms to transform time series or a set of time series to the network: recurrent and graph visibility. For the received network designed dynamic spectral, topological and multiplex measures of complexity. For example, the daily values the stock indices show that most of the complexity measures behaving in a ch

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Alves, Jose-Henrique, Roberto Padilla-Hernandez, Deanna Spindler, et al. Development of a wave model component in the first coupled Global Ensemble Forecast System at NOAA. Engineer Research and Development Center (U.S.), 2025. https://doi.org/10.21079/11681/49784.

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We describe the development of the wave component in the first global-scale coupled operational forecast system using the Unified Forecasting System at NOAA, part of the U.S. National Weather Service operational forecasting suite. The operational implementation of the atmosphere–wave coupled Global Ensemble Forecast System, version 12, was a critical step in NOAA’s transition to the broader community based UFS framework. GEFSv12 represents a significant advancement, extending forecast ranges and empowering the NWS to deliver advanced weather predictions with extended lead times for high-impact

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Si, Hongjun, Saburoh Midorikawa, and Tadahiro Kishida. Development of NGA-Sub Ground-Motion Model of 5%-Damped Pseudo-Spectral Acceleration Based on Database for Subduction Earthquakes in Japan. Pacific Earthquake Engineering Research Center, University of California, Berkeley, CA, 2020. http://dx.doi.org/10.55461/lien3652.

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Presented within is an empirical ground-motion model (GMM) for subduction-zone earthquakesin Japan. The model is based on the extensive and comprehensive subduction database of Japanese earthquakes by the Pacific Engineering Research Center (PEER). It considers RotD50 horizontal components of peak ground acceleration (PGA), peak ground velocity (PGV), and 5%-damped elastic pseudo-absolute acceleration response spectral ordinates (PSA) at the selected periods ranging from 0.01 to 10 sec. The model includes terms and predictor variables considering tectonic setting (i.e., interplate and intrasla

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Pompeu, Gustavo, and José Luiz Rossi. Real/Dollar Exchange Rate Prediction Combining Machine Learning and Fundamental Models. Inter-American Development Bank, 2022. http://dx.doi.org/10.18235/0004491.

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The study of the predictability of exchange rates has been a very recurring theme on the economics literature for decades, and very often is not possible to beat a random walk prediction, particularly when trying to forecast short time periods. Although there are several studies about exchange rate forecasting in general, predictions of specifically Brazilian real (BRL) to United States dollar (USD) exchange rates are very hard to find in the literature. The objective of this work is to predict the specific BRL to USD exchange rates by applying machine learning models combined with fundamental

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Duffie, Darrell, and Ke Wang. Multi-Period Corporate Failure Prediction with Stochastic Covariates. National Bureau of Economic Research, 2004. http://dx.doi.org/10.3386/w10743.

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Duffie, Darrell, Leandro Siata, and Ke Wang. Multi-Period Corporate Default Prediction With Stochastic Covariates. National Bureau of Economic Research, 2006. http://dx.doi.org/10.3386/w11962.

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Dandekar, B. S., and J. Buchau. Improving foF2 Prediction for the Sunrise Transition Period. Defense Technical Information Center, 1986. http://dx.doi.org/10.21236/ada170457.

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