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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 kurtosis criteria were first applied for data decomposition and construction of trend and periodic components. Second, a polynomial regression model and RNN with maximal information coefficient (MIC)-based input variable selection were implemented for individual prediction of trend and periodic components independently. Finally, the predictions of trend and periodic components were aggregated into a final ensemble prediction. The experimental results from the Muyubao landslide demonstrate that the proposed EEMD-RNN decomposition-ensemble learning model is capable of increasing prediction accuracy and outperforms the traditional decomposition-ensemble learning models (including EEMD-support vector machine, and EEMD-extreme learning machine). Moreover, compared with standard RNN, the gated recurrent unit (GRU)-and long short-term memory (LSTM)-based models perform better in predicting accuracy. The EEMD-RNN decomposition-ensemble learning model is promising for landslide displacement prediction.
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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.
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 short-term speed prediction under different scenarios: (1) multi-horizon ahead prediction (5, 15, 30, 60 minutes ahead predictions), (2) with and without periodic component, (3) two data aggregation levels (5-minute and 15-minute), (4) peak hours and off-peak hours. Specifically, three statistical models (i.e., space time (ST) model, vector autoregressive (VAR) model, autoregressive integrated moving average (ARIMA) model) and three machine learning approaches (i.e., support vector machines (SVM) model, multi-layer perceptron (MLP) model, recurrent neural network (RNN) model) are developed and examined. Furthermore, the periodic features of the speed data are considered via a hybrid prediction method, which assumes that the data consist of two components: a periodic component and a residual component. The periodic component is described by a trigonometric regression function, and the residual component is modeled by the statistical models or the machine learning approaches. The important conclusions can be summarized as follows: (1) the multi-step ahead prediction accuracy improves when considering the periodic component of speed data for both three statistical models and three machine learning models, especially in the peak hours; (2) considering the impact of periodic component for all models, the prediction performance improvement gradually becomes larger as the time step increases; (3) under the same prediction horizon, the prediction performance of all models for 15-minute speed data is generally better than that for 5-minute speed data. Overall, the findings in this paper suggest that the proposed hybrid prediction approach is effective for both statistical and machine learning models in short-term speed prediction.
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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.
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 model). In order to produce more accurate predictions, background-value optimization is specially carried out using the particle swarm optimization technique in conventional GM (1, 1). For verifying PGMSW, it is utilized in the prediction of the cyclic fluctuation of telemetry series data and exponential variations therein. The simulation results indicate that the PGMSW model provides accurate solutions for prediction problems similar to the pseudo-periodic telemetry series.
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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 (January 2017): 52–66. http://dx.doi.org/10.4018/ijalr.2017010104.
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 property. In general, future prediction using machine learning is based on the tendency obtained through past training or learning. In this case, an optimal action will be determined uniquely based on a property of disturbance. However, in this type of situation, the learning time increases in proportional to the amount of training data, either, the tendency may not be found using prediction, in the worst case. In this paper, we focus on prediction for almost-periodic disturbance. In particular, we consider the situation where almost-periodic disturbance signals occur. From this perspective, we propose a method that identifies the frequency of an almost- periodic function based on the frequency of the disturbance using Fourier transform, nearest-neighbor one-step-ahead forecasts and Nyquist-Shannon sampling theorem.
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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 (May 25, 2023): e0286325. http://dx.doi.org/10.1371/journal.pone.0286325.
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 used to adjust the proportion of the predicted value of the fuzzy time series and the periodic observation value. A fund revenue prediction model and a fund expenditure prediction model are constructed, and an experiment is carried out with the actual financial data of a university in China. The experimental result shows the effectiveness of the proposed model, which can provide solid references for financial management and funds risk prevention in universities.
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Cheng, Weiwei, Guigen Nie, and Jian Zhu. "Characterizing Periodic Variations of Atomic Frequency Standards via Their Frequency Stability Estimates." Sensors 23, no. 11 (June 5, 2023): 5356. http://dx.doi.org/10.3390/s23115356.
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 are independent of the variances of the stochastic component. The proposed model is tested against simulated and real clock data, revealing that our approach provides more precise characterization of periodic variations compared to the least squares method. Additionally, we observe that overfitting periodic variations can improve the precision of GPS clock bias prediction, as indicated by a comparison of fitting and prediction errors of satellite clock bias.
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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 (September 2001): 407–52. http://dx.doi.org/10.1016/s0039-3681(01)00023-1.
Pawelzik, K., and H. G. Schuster. "Unstable periodic orbits and prediction." Physical Review A 43, no. 4 (February 1, 1991): 1808–12. http://dx.doi.org/10.1103/physreva.43.1808.
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.
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 compared the impacts of three periodic functions on multistep ahead travel time prediction for different temporal scales (5-minute, 10-minute, and 15-minute). The results indicate that the periodic functions can improve the prediction performance of machine learning models for more than 60 minutes ahead prediction and improve the over 30 minutes ahead prediction accuracy for statistical models. Three periodic functions show a slight difference in improving the prediction accuracy of the six prediction models. For the same prediction step, the effect of the periodic function is more obvious at a higher level of aggregation.
The periodic noise exists in BeiDou navigation satellite system (BDS) clock offsets. As a commonly used satellite clock prediction model, the spectral analysis model (SAM) typically detects and identifies the periodic terms by the Fast Fourier transform (FFT) according to long-term clock offset series. The FFT makes an aggregate assessment in frequency domain but cannot characterize the periodic noise in a time domain. Due to space environment changes, temperature variations, and various disturbances, the periodic noise is time-varying, and the spectral peaks vary over time, which will affect the prediction accuracy of the SAM. In this paper, we investigate the periodic noise and its variations present in BDS clock offsets, and improve the clock prediction model by considering the periodic variations. The periodic noise and its variations over time are analyzed and quantified by short time Fourier transform (STFT). The results show that both the amplitude and frequency of the main periodic term in BDS clock offsets vary with time. To minimize the impact of periodic variations on clock prediction, a time frequency analysis model (TFAM) based on STFT is constructed, in which the periodic term can be quantified and compensated accurately. The experiment results show that both the fitting and prediction accuracy of TFAM are better than SAM. Compared with SAM, the average improvement of the prediction accuracy using TFAM of the 6 h, 12 h, 18 h and 24 h is in the range of 6.4% to 10% for the GNSS Research Center of Wuhan University (WHU) clock offsets, and 11.1% to 14.4% for the Geo Forschungs Zentrum (GFZ) clock offsets. For the satellites C06, C14, and C32 with marked periodic variations, the prediction accuracy is improved by 26.7%, 16.2%, and 16.3% for WHU clock offsets, and 29.8%, 16.0%, 21.0%, and 9.0% of C06, C14, C28, and C32 for GFZ clock offsets.
Dissertations / Theses on the topic "Periodic prediction":
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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.
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.
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.
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 for NDA and 6DOF simulation in following waves are conducted for complete modeling of broaching.
CFD parametric rolling simulations show remarkably close agreement with EFD. The CFD stabilized roll angle is very close to those of EFD but CFD predicts larger instability zones. The CFD and EFD results are analyzed with consideration ship theory and compared with NDA. NDA predictions are in qualitative agreement with CFD and EFD.
CFD and EFD full Fr curve resistance, static heel and drift in calm water, and static heel in following waves results show fairly close agreement. CFD shows reasonable agreement for static heel and drift linear maneuvering derivatives, whereas large errors are indicated for nonlinear derivatives. The CFD and EFD results are analyzed with consideration ship theory and compared with NDA models. The surge force in following wave is also estimated from Potential Theory and compared with CFD and EFD. It is shown that CFD reproduces the decrease of the surge force near the Fr of 0.2 whereas Potential Theory fails.
The CFD broaching simulations are performed for series of heading and Fr and results are compared with the predictions of NDA based on CFD, EFD, and Potential Theory inputs. CFD free model simulations show promising results predicting the instability boundary accurately. CFD calculation of wave and rudders yaw moment explains the processes of surf-riding, broaching, and periodic motion. The NDA simulation using CFD and Potential Flow inputs suggests that CFD/ Potential Flow can be considered as replacement for EFD 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.
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 stabilized orbit. Local stability of the periodic orbits is analyzed by studying the stability of some linear time-periodic systems, using the Floquet stability theory. These linear systems are obtained by linearizing the trajectories in the vicinity of the periodic orbits.The control methods used for stabilization of periodic orbits here are the proportional feedback control, the delayed feedback control and the prediction-based feedback control. These methods are applied to discrete and continuous-time systems with the necessary modifications. The main contributions of the thesis are related to these methods, proposing an alternative control gain design, a new control law and related results.
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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.
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.
Mestrado em Ciências Empresariais 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 prática é exposta, juntamente com alguns resultados de análises semelhantes de diferentes mercados em todo o mundo, como uma maneira de obter de alguns exemplos da forma como a função "linear" log-periódica de potências funciona.
Apresento alguns contextos onde o tempo de crise é apresentado aos mercados internacionais - como uma maneira de demonstração de antecedentes -, assim como apresento também três aplicações práticas do mercado de acções português (1997, 2008 e 2015). A sensibilidade dos resultados e do significado das oscilações log-periódicas são avaliadas.
Concluo com algumas recomendações e futuras propostas de investigação. The study of critical phenomena that originated in the natural sciences and found many fields of applications has been extended in the last years to the financial economics? field, giving researchers new approaches to known problems, namely those related to risk management, forecasting, the study of bubbles and crashes, and many kind of problems involving complex systems with self-organized criticality.
The theory of self-similar oscillatory time singularities is presented. A practical methodology is exposed along with some results from similar analysis from different markets around the world, as a way to get some examples of the way the ´Linear´ Log-Periodic Power Law formula works.
Some context presenting the international markets at the time of crisis is given as a way of having some background, and three practical applications for the Portuguese stock market are made (1997, 2008 and 2015). The sensitivity of the results and the significance from the log-periodic oscillations is assessed.
It concludes with some recommendations and future proposed research.
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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.
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 application tasks at run-time for adherence to the desired real-time requirements, detects timing failures or trends for impending failures (due to workload fluctuations), and dynamically allocate resources by replicating subtasks of application tasks for load sharing. We present "predictive" resource allocation algorithms that determine the number of subtask replicas that are required for adapting the application to a given
workload situation using statistical regression theory. The algorithms use regression equations that forecast subtask timeliness as a function of external load parameters such as number of sensor reports and internal resource load parameters such as CPU utilization. The regression equations are determined off-line and on-line from application profiles that are collected off-line and on-line, respectively. To evaluate the performance of the predictive algorithms, we consider algorithms that determine the number of subtask replicas using empirically determined functions. The empirical functions compute the number of replicas as a function of the rate of change in the application workload during a "window" of past task periods. We implemented the resource management algorithms as part of a middleware infrastructure and measured the performance of the algorithms using a real-time benchmark. The experimental results indicate that the predictive, regression theory-based algorithms generally produce lower missed
deadline ratios than the empirical strategies under the workload conditions that were studied. Master of Science
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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.
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, Firstly, to have a new application for CWT (Collapsed Weighted Tensor) method. Secondly, to modify the CWT method to predict the location of a user. Thirdly, to provide a suitable method for the given Telenor dataset to predict the user’s location over a period of time. Methods. The thesis work has been carried out by implementing the modified CWT method. The predicted location obtained by modified CWT cannot be determined to which time stamp it belongs as the given Telenor dataset contains missing time stamps. So, the modified CWT method is implemented in two different methods. Replacing missing values with first value in dataset. Replacing missing values with second value in dataset. These two methods are implemented and determined which method can predict the location of users with minimal error. Results. The results are carried by assuming that the given Telenor dataset for one week will be same as that for the next week. Users are selected in a random sample and above mentioned methods are performed. Furthermore, RMSD values and computational time are calculated for each method and selected users. Conclusion. Based on the analysis of the results, Firstly, it can be concluded that CWT method have been modified and used for predicting the user’s location for next time stamp. Secondly, the method can be extended to predict over a period of time. Finally, modified CWT method predicts location of the user with minimal error when missing values are replaced by first value in the dataset.
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Levin, Ori. "Numerical studies of transtion in wall-bounded flows." Doctoral thesis, KTH, Mechanics, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-546.
Disturbances introduced in wall-bounded flows can grow and lead to transition from laminar to turbulent flow. In order to reduce losses or enhance mixing in energy systems, a fundamental understanding of the flow stability and transition mechanism is important. In the present thesis, the stability, transition mechanism and early turbulent evolution of wall-bounded flows are studied. The stability is investigated by means of linear stability equations and the transition mechanism and turbulence are studied using direct numerical simulations. Three base flows are considered, the Falkner-Skan boundary layer, boundary layers subjected to wall suction and the Blasius wall jet. The stability with respect to the exponential growth of waves and the algebraic growth of optimal streaks is studied for the Falkner-Skan boundary layer. For the algebraic growth, the optimal initial location, where the optimal disturbance is introduced in the boundary layer, is found to move downstream with decreased pressure gradient. A unified transition prediction method incorporating the influences of pressure gradient and free-stream turbulence is suggested. The algebraic growth of streaks in boundary layers subjected to wall suction is calculated. It is found that the spatial analysis gives larger optimal growth than temporal theory. Furthermore, it is found that the optimal growth is larger if the suction begins a distance downstream of the leading edge. Thresholds for transition of periodic and localized disturbances as well as the spreading of turbulent spots in the asymptotic suction boundary layer are investigated for Reynolds number Re=500, 800 and 1200 based on the displacement thickness and the free-stream velocity. It is found that the threshold amplitude scales like Re^-1.05 for transition initiated by streamwise vortices and random noise, like Re^-1.3 for oblique transition and like Re^-1.5 for the localized disturbance. The turbulent spot is found to take a bullet-shaped form that becomes more distinct and increases its spreading rate for higher Reynolds number. The Blasius wall jet is matched to the measured flow in an experimental wall-jet facility. Both the linear and nonlinear regime of introduced waves and streaks are investigated and compared to measurements. It is demonstrated that the streaks play an important role in the breakdown process where they suppress pairing and enhance breakdown to turbulence. Furthermore, statistics from the early turbulent regime are analyzed and reveal a reasonable self-similar behavior, which is most pronounced with inner scaling in the near-wall region.
Dolph, K. Leroy. Prediction of periodic basal area increment for young-growth mixed conifers in the Sierra Nevada. Berkeley, Calif: U.S. Dept. of Agriculture, Forest Service, Pacific Southwest Forest and Range Experiment Station, 1988.
Moreno, Alejandro García. El paisaje del valle del Asón (Cantabria) a finales del Tardiglaciar: Un modelo predictivo de vegetación arbórea mediante SIG = Landscape in the Ason River Valley (Spain) during the Final Late Glacial : a predictive vegetation model using GIS. Oxford: Archaeopress, 2015.
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 in the postoperative period. The section of assessment of the structural and functional state of lymphocytes in the development of complications in the postoperative period by assessing the blebbing of the plasma membrane of the cell is presented.
It is intended for urologists, general surgeons, residents studying in the specialty "Urology". It can be useful for doctors of other specialties and senior students of higher medical educational institutions.
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Terehin, Valeriy, and Viktor Chernyshov. Efficiency and effectiveness of the penitentiary system: assessment and planning. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1079434.
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 court sentence. Criminal potentials are estimated by predictive values of the aggregate of three groups of characteristics of the criminal potential of convicts, determined by the stages of the cycle of recidivism. The practical results of the use of sound methods and developed tools are based on the use of a significant amount of empirical data on the institutions of the criminal justice system and its systematic expert and statistical analysis. The monograph is a generalization and development of the works carried out by the authors during 2012-2017 in the process of preparing masters of Management for the penal system. It is intended for managers and specialists of the bodies and institutions of the Criminal Justice System, researchers, teachers of higher educational institutions who train specialists for law enforcement agencies.
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Wang, Bin. Intraseasonal Modulation of the Indian Summer Monsoon. Oxford University Press, 2018. http://dx.doi.org/10.1093/acrefore/9780190228620.013.616.
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 and wet events are regulated by the boreal summer ISO (BSISO). The BSISO over Indian monsoon region consists of northward propagating 30–60 day and westward propagating 10–20 day modes. The “clustering” of synoptic activity was separately modulated by both the 30–60 day and 10–20 day BSISO modes in approximately equal amounts. The clustering is particularly strong when the enhancement effect from both modes acts in concert. The northward propagation of BSISO is primarily originated from the easterly vertical shear (increasing easterly winds with height) of the monsoon flows, which by interacting with the BSISO convective system can generate boundary layer convergence to the north of the convective system that promotes its northward movement. The BSISO-ocean interaction through wind-evaporation feedback and cloud-radiation feedback can also contribute to the northward propagation of BSISO from the equator. The 10–20 day oscillation is primarily produced by convectively coupled Rossby waves modified by the monsoon mean flows. Using coupled general circulation models (GCMs) for ISO prediction is an important advance in subseasonal forecasts. The major modes of ISO over Indian monsoon region are potentially predictable up to 40–45 days as estimated by multiple GCM ensemble hindcast experiments. The current dynamical models’ prediction skills for the large initial amplitude cases are approximately 20–25 days, but the prediction of developing BSISO disturbance is much more difficult than the prediction of the mature BSISO disturbances. This article provides a synthesis of our current knowledge on the observed spatial and temporal structure of the ISO over India and the important physical processes through which the BSISO regulates the ISM active-break cycles and severe weather events. Our present capability and shortcomings in simulating and predicting the monsoon ISO and outstanding issues are also discussed.
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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.
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 onset approaches, the excitability of the underlying neuronal networks tends to increase. This assumption is being explored using not only the ongoing EEG but also active probes, applying appropriate stimuli to brain areas to estimate the excitability of the neuronal populations. Experimental results support this assumption, suggesting that it may be possible to develop paradigms to estimate the risk of an impending transition to an epileptic state.
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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.
Lima-de-Faria, A. Periodic Tables Unifying Living Organisms at the Molecular Level: The Predictive Power of the Law of Periodicity. World Scientific Publishing Co Pte Ltd, 2018.
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.
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 calibration in the context of Bayes’ theorem, which reveals critically important realities involving base rates and the use of currently available standardization samples in determining a final estimate of recidivism likelihood. The essay concludes with a glimpse into the future of risk assessment and predictions about the next stage in evidence-based risk assessment of sex offenders.
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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.
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 vote in 2004’, or the traded probability, of Bush winning the election. These data are used to build a measure of information flow. The results show that information flows increased as a result of the televised debates, and that these debates, along with the selection of the vice presidential candidate, increased prediction market volatility.
McCormick, Andrew C., and Asoke K. Nandi. "Condition Monitoring Using Periodic Time-Varying AR Models." In Signal Analysis and Prediction, 197–204. Boston, MA: Birkhäuser Boston, 1998. http://dx.doi.org/10.1007/978-1-4612-1768-8_14.
Bračič, Maja, and Aneta Stefanovska. "Lyapunov Exponents of Simulated and Measured Quasi-Periodic Flows." In Signal Analysis and Prediction, 479–88. Boston, MA: Birkhäuser Boston, 1998. http://dx.doi.org/10.1007/978-1-4612-1768-8_34.
Burgess, Keith, Katie Burgess, Prajan Subedi, Phil Ainslie, Zbigniew Topor, and William Whitelaw. "Prediction of Periodic Breathing at Altitude." In Integration in Respiratory Control, 442–46. New York, NY: Springer New York, 2008. http://dx.doi.org/10.1007/978-0-387-73693-8_77.
Michalak, Marcin. "Time Series Prediction with Periodic Kernels." In Computer Recognition Systems 4, 137–46. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20320-6_15.
Blatov, Vladislav A., and Davide M. Proserpio. "Periodic-Graph Approaches in Crystal Structure Prediction." In Modern Methods of Crystal Structure Prediction, 1–28. Weinheim, Germany: Wiley-VCH Verlag GmbH & Co. KGaA, 2010. http://dx.doi.org/10.1002/9783527632831.ch1.
Cao, Yongzhong, He Zhou, and Bin Li. "Rice Growth Prediction Based on Periodic Growth." In Studies in Computational Intelligence, 159–75. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-56178-9_13.
Zhao, Jijun, Hao Liu, Zhihua Li, and Wei Li. "Periodic Data Prediction Algorithm in Wireless Sensor Networks." In Communications in Computer and Information Science, 695–701. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-36252-1_65.
Bittanti, Sergio. "The periodic prediction problem for cyclostationary processes — an introduction." In Modelling, Robustness and Sensitivity Reduction in Control Systems, 239–49. Berlin, Heidelberg: Springer Berlin Heidelberg, 1987. http://dx.doi.org/10.1007/978-3-642-87516-8_15.
Karimi, M., P. Croaker, and N. Kessissoglou. "Trailing-Edge Noise Prediction Using a Periodic BEM Technique." In Fluid-Structure-Sound Interactions and Control, 39–44. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-48868-3_6.
Bouzayane, Sarra, and Ines Saad. "Intelligent Multicriteria Decision Support System for a Periodic Prediction." In Decision Support Systems IX: Main Developments and Future Trends, 97–110. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-18819-1_8.
Conference papers on the topic "Periodic prediction":
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Gillan, Mark, R. Mitchell, S. Raghunathan, Jonathan Cole, Mark Gillan, R. Mitchell, S. Raghunathan, and Jonathan Cole. "Prediction and control of periodic flows." In 35th Aerospace Sciences Meeting and Exhibit. Reston, Virigina: American Institute of Aeronautics and Astronautics, 1997. http://dx.doi.org/10.2514/6.1997-832.
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.
Hu, Xiaobo, and Gang Quan. "Fast performance prediction for periodic task systems." In the eighth international workshop. New York, New York, USA: ACM Press, 2000. http://dx.doi.org/10.1145/334012.334026.
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.
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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Zonoozi, Ali, Jung-jae Kim, Xiao-Li Li, and Gao Cong. "Periodic-CRN: A Convolutional Recurrent Model for Crowd Density Prediction with Recurring Periodic Patterns." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/519.
Time-series forecasting in geo-spatial domains has important applications, including urban planning, traffic management and behavioral analysis. We observed recurring periodic patterns in some spatio-temporal data, which were not considered explicitly by previous non-linear works. To address this lack, we propose novel `Periodic-CRN' (PCRN) method, which adapts convolutional recurrent network (CRN) to accurately capture spatial and temporal correlations, learns and incorporates explicit periodic representations, and can be optimized with multi-step ahead prediction. We show that PCRN consistently outperforms the state-of-the-art methods for crowd density prediction across two taxi datasets from Beijing and Singapore.
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Wu, Jiaqing, Yinzhi Wu, and Cheng Chen. "Periodic Attention Networks for Air Quality Index Prediction." In ICMLCA 2023: 2023 4th International Conference on Machine Learning and Computer Application. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3650215.3650271.
Luo, Albert C. J. "Stability and Bifurcation for the Equispaced, Periodic Motion of a Horizontal Impact Damper." In ASME 2001 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2001. http://dx.doi.org/10.1115/detc2001/vib-21505.
Abstract Stability and bifurcation conditions for the asymmetric, periodic motion of a horizontal impact damper under a periodic excitation are developed through four mappings for two switch-planes relative to discontinuities. Period-doubling bifurcation for equispaced motion does not occur, but the asymmetric period-1 motions change to the asymmetric, period-2 ones through a period doubling bifurcation. A numerical prediction for equispaced to chaotic motions is completed. The numerical and analytical predictions of the periodic motion are in very good agreement. The asymmetric, periodic motions are also simulated.
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Guo, Xiaogang, Guangyue Li, Zhixing Chen, Huazu Zhang, Yulin Ding, Jinghan Wang, Zilong Zhao, and Luliang Tang. "Large-Scale Human Mobility Prediction Based on Periodic Attenuation and Local Feature Match." In HuMob-Challenge '23: 1st International Workshop on the Human Mobility Prediction Challenge. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3615894.3628505.
Xing, Siyuan, and Albert C. J. Luo. "Periodic Motions in a First-Order, Time-Delayed, Nonlinear System." In ASME 2018 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/imece2018-86824.
In this paper, periodic motions in a first-order, time-delayed, nonlinear system are investigated. For time-delay terms of non-polynomial functions, the traditional analytical methods have difficulty in determining periodic motions. The semi-analytical method is used for prediction of periodic motion. This method is based on implicit mappings obtained from discretization of the original differential equation. From the periodic nodes, the corresponding approximate analytical expression can be obtained through discrete finite Fourier series. The stability and the bifurcations of such periodic motions are determined by eigenvalue analysis. The bifurcation tree of period-1 to period-4 motions are obtained and the numerical results and analytical predictions are compared. The complexity of periodic motions in such a simple dynamical system is discussed.
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Ozcan Kini, Seldag, and Ayse Tosun. "[Research Paper] Periodic Developer Metrics in Software Defect Prediction." In 2018 IEEE 18th International Working Conference on Source Code Analysis and Manipulation (SCAM). IEEE, 2018. http://dx.doi.org/10.1109/scam.2018.00016.
Dolph, Leroy K. Prediction of periodic basal area increment for young-growth mixed conifers in sierra Nevada. Berkeley, CA: U.S. Department of Agriculture, Forest Service, Pacific Southwest Forest and Range Experiment Station, 1988. http://dx.doi.org/10.2737/psw-rp-190.
Leis. L51865 Hydrotest Parameters to Help Control High-pH SCC on Gas Transmission Pipelines. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), September 1999. http://dx.doi.org/10.55274/r0010208.
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 simulated cracking populations and incident occurrence and that observed for operating pipelines behavior. Because methods to simulate coating failure have not yet been developed, the model assumes the presence of a cracking environment, which according to field experience adds 3 years or more to the simulated pipeline life.
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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. Madrid: Banco de España, March 2024. http://dx.doi.org/10.53479/36137.
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 indicators, rather than leading in the case of some of them; (ii) by using a stochastic process to model the variance in the estimated common component, such variance may now vary over time; (iii) the set of indicators has been revised in order to include only those that provide the most relevant information when it comes to predicting post-pandemic GDP growth. These modifications yield a substantial improvement in the predictive power of Spain-STING in the post-pandemic period, and maintain such predictive power for the pre-pandemic period.
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Соловйов, В. М., and В. В. Соловйова. Моделювання мультиплексних мереж. Видавець Ткачук О.В., 2016. http://dx.doi.org/10.31812/0564/1253.
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 characteristic way in time periods that characterize the different phases of the behavior and state of the stock market. This fact encouraged to use monitoring and prediction of critical and crisis states in socio-economic systems.
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Duffie, Darrell, and Ke Wang. Multi-Period Corporate Failure Prediction with Stochastic Covariates. Cambridge, MA: National Bureau of Economic Research, September 2004. http://dx.doi.org/10.3386/w10743.
Duffie, Darrell, Leandro Siata, and Ke Wang. Multi-Period Corporate Default Prediction With Stochastic Covariates. Cambridge, MA: National Bureau of Economic Research, January 2006. http://dx.doi.org/10.3386/w11962.
Dandekar, B. S., and J. Buchau. Improving foF2 Prediction for the Sunrise Transition Period. Fort Belvoir, VA: Defense Technical Information Center, January 1986. http://dx.doi.org/10.21236/ada170457.
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, December 2020. http://dx.doi.org/10.55461/lien3652.
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 intraslab), hypocentral depths (D), magnitude scaling, distance attenuation, and site response. The magnitude scaling derived in this study is well constrained by the data observed during the large-magnitude interface events in Japan (i.e., the 2003 Tokachi-Oki and 2011 Tohoku earthquakes) for different periods. The developed ground-motion prediction equation (GMPE) covers subduction-zone earthquakes that have occurred in Japan for magnitudes ranging from 5.5 to as large as 9.1, with distances less than 300 km from the source.
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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, September 2022. http://dx.doi.org/10.18235/0004491.
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 theories from macroeconomics, such as monetary and Taylor rule models, and compare the results to those of a random walk model by using the root mean squared error (RMSE) and the Diebold-Mariano (DM) test. We show that it is possible to beat the random walk by these metrics.
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Gunay, Selim, Fan Hu, Khalid Mosalam, Arpit Nema, Jose Restrepo, Adam Zsarnoczay, and Jack Baker. Blind Prediction of Shaking Table Tests of a New Bridge Bent Design. Pacific Earthquake Engineering Research Center, University of California, Berkeley, CA, November 2020. http://dx.doi.org/10.55461/svks9397.
Considering the importance of the transportation network and bridge structures, the associated seismic design philosophy is shifting from the basic collapse prevention objective to maintaining functionality on the community scale in the aftermath of moderate to strong earthquakes (i.e., resiliency). In addition to performance, the associated construction philosophy is also being modernized, with the utilization of accelerated bridge construction (ABC) techniques to reduce impacts of construction work on traffic, society, economy, and on-site safety during construction. Recent years have seen several developments towards the design of low-damage bridges and ABC. According to the results of conducted tests, these systems have significant potential to achieve the intended community resiliency objectives. Taking advantage of such potential in the standard design and analysis processes requires proper modeling that adequately characterizes the behavior and response of these bridge systems. To evaluate the current practices and abilities of the structural engineering community to model this type of resiliency-oriented bridges, the Pacific Earthquake Engineering Research Center (PEER) organized a blind prediction contest of a two-column bridge bent consisting of columns with enhanced response characteristics achieved by a well-balanced contribution of self-centering, rocking, and energy dissipation. The parameters of this blind prediction competition are described in this report, and the predictions submitted by different teams are analyzed. In general, forces are predicted better than displacements. The post-tension bar forces and residual displacements are predicted with the best and least accuracy, respectively. Some of the predicted quantities are observed to have coefficient of variation (COV) values larger than 50%; however, in general, the scatter in the predictions amongst different teams is not significantly large. Applied ground motions (GM) in shaking table tests consisted of a series of naturally recorded earthquake acceleration signals, where GM1 is found to be the largest contributor to the displacement error for most of the teams, and GM7 is the largest contributor to the force (hence, the acceleration) error. The large contribution of GM1 to the displacement error is due to the elastic response in GM1 and the errors stemming from the incorrect estimation of the period and damping ratio. The contribution of GM7 to the force error is due to the errors in the estimation of the base-shear capacity. Several teams were able to predict forces and accelerations with only moderate bias. Displacements, however, were systematically underestimated by almost every team. This suggests that there is a general problem either in the assumptions made or the models used to simulate the response of this type of bridge bent with enhanced response characteristics. Predictions of the best-performing teams were consistently and substantially better than average in all response quantities. The engineering community would benefit from learning details of the approach of the best teams and the factors that caused the models of other teams to fail to produce similarly good results. Blind prediction contests provide: (1) very useful information regarding areas where current numerical models might be improved; and (2) quantitative data regarding the uncertainty of analytical models for use in performance-based earthquake engineering evaluations. Such blind prediction contests should be encouraged for other experimental research activities and are planned to be conducted annually by PEER.
