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Journal articles on the topic 'Real time prediction'

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Author: Grafiati

Published: 4 June 2021

Last updated: 26 January 2023

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1

Fridovich-Keil, David, Andrea Bajcsy, Jaime F. Fisac, Sylvia L. Herbert, Steven Wang, Anca D. Dragan, and Claire J. Tomlin. "Confidence-aware motion prediction for real-time collision avoidance1." International Journal of Robotics Research 39, no. 2-3 (June 24, 2019): 250–65. http://dx.doi.org/10.1177/0278364919859436.

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One of the most difficult challenges in robot motion planning is to account for the behavior of other moving agents, such as humans. Commonly, practitioners employ predictive models to reason about where other agents are going to move. Though there has been much recent work in building predictive models, no model is ever perfect: an agent can always move unexpectedly, in a way that is not predicted or not assigned sufficient probability. In such cases, the robot may plan trajectories that appear safe but, in fact, lead to collision. Rather than trust a model’s predictions blindly, we propose that the robot should use the model’s current predictive accuracy to inform the degree of confidence in its future predictions. This model confidence inference allows us to generate probabilistic motion predictions that exploit modeled structure when the structure successfully explains human motion, and degrade gracefully whenever the human moves unexpectedly. We accomplish this by maintaining a Bayesian belief over a single parameter that governs the variance of our human motion model. We couple this prediction algorithm with a recently proposed robust motion planner and controller to guide the construction of robot trajectories that are, to a good approximation, collision-free with a high, user-specified probability. We provide extensive analysis of the combined approach and its overall safety properties by establishing a connection to reachability analysis, and conclude with a hardware demonstration in which a small quadcopter operates safely in the same space as a human pedestrian.

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Dissanayake, Vipula, Sachini Herath, Sanka Rasnayaka, Sachith Seneviratne, Rajith Vidanaarachchi, and Chandana Gamage. "Real-Time Gesture Prediction Using Mobile Sensor Data for VR Applications." International Journal of Machine Learning and Computing 6, no. 3 (June 2016): 215–19. http://dx.doi.org/10.18178/ijmlc.2016.6.3.600.

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3

Huitian Lu, W. J. Kolarik, and S. S. Lu. "Real-time performance reliability prediction." IEEE Transactions on Reliability 50, no. 4 (2001): 353–57. http://dx.doi.org/10.1109/24.983393.

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4

Georgakakos, Konstantine P. "Real-time flash flood prediction." Journal of Geophysical Research 92, no. D8 (1987): 9615. http://dx.doi.org/10.1029/jd092id08p09615.

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5

Zissis, Dimitrios, Elias K. Xidias, and Dimitrios Lekkas. "Real-time vessel behavior prediction." Evolving Systems 7, no. 1 (March 24, 2015): 29–40. http://dx.doi.org/10.1007/s12530-015-9133-5.

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6

Maber, J., P. Dewar, J. P. Praat, and A. J. Hewitt. "REAL TIME SPRAY DRIFT PREDICTION." Acta Horticulturae, no. 566 (December 2001): 493–98. http://dx.doi.org/10.17660/actahortic.2001.566.64.

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7

AHALPARA, DILIP P., and JITENDRA C. PARIKH. "MODELING TIME SERIES DATA OF REAL SYSTEMS." International Journal of Modern Physics C 18, no. 02 (February 2007): 235–52. http://dx.doi.org/10.1142/s0129183107010474.

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Dynamics of complex systems is studied by first considering a chaotic time series generated by Lorenz equations and adding noise to it. The trend (smooth behavior) is separated from fluctuations at different scales using wavelet analysis and a prediction method proposed by Lorenz is applied to make out of sample predictions at different regions of the time series. The prediction capability of this method is studied by considering several improvements over this method. We then apply this approach to a real financial time series. The smooth time series is modeled using techniques of non linear dynamics. Our results for predictions suggest that the modified Lorenz method gives better predictions compared to those from the original Lorenz method. Fluctuations are analyzed using probabilistic considerations.

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8

Březková, L., M. Starý, and P. Doležal. "The real-time stochastic flow forecast." Soil and Water Research 5, No. 2 (May 24, 2010): 49–57. http://dx.doi.org/10.17221/13/2009-swr.

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In the Czech Republic, deterministic flow forecasts with the lead time of 48 hours, calculated by rainfall-runoff models for basins of a size of several hundreds to thousands square kilometers, are nowadays a common part of the operational hydrological service. The Czech Hydrometeorological Institute (CHMI) issues daily the discharge forecast for more than one hundred river profiles. However, the causal rainfall is a random process more than a deterministic one, therefore the deterministic discharge forecast based on one precipitation prediction is a significant simplification of the reality. Since important decisions must be done during the floods, it is necessary to take into account the indeterminity of the input meteorological data and to express the uncertainty of the resulting discharge forecast. In the paper, a solution of this problem is proposed. The time series of the input precipitation prediction data have been generated repeatedly (by the Monte Carlo method) and, subsequently, the set of discharge forecasts based on the repeated hydrological model simulations has been obtained and statistically evaluated. The resulting output can be, for example, the range of predicted peak discharges, the peak discharge exceeding curve or the outflow volume exceeding curve. The properties of the proposed generator have been tested with acceptable results on several flood events which occurred over the last years in the upper part of the Dyje catchment (Podhradí closing profile). The rainfall-runoff model HYDROG, which has been in operation in CHMI since 2003, was used for hydrological simulation.

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Brown, Andrew, and Toby Gifford. "Prediction and Proactivity in Real-Time Interactive Music Systems." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 9, no. 5 (June 30, 2021): 35–39. http://dx.doi.org/10.1609/aiide.v9i5.12644.

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We advocate for the use of predictive techniques in interactive computer music systems. We suggest that the inclusion of prediction can assist in the design of proactive rather than reactive computational performance partners. We summarize the significant role prediction plays in human musical decisions, and the only modest use of prediction in interactive music systems to date. After describing how we are working toward employing predictive processes in our own metacreation software we reflect on future extensions to these approaches.

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10

Aboudina, Aya, Ehab Diab, and Amer Shalaby. "Predictive Analytics of Streetcar Bunching Occurrence Time for Real-Time Applications." Transportation Research Record: Journal of the Transportation Research Board 2675, no. 6 (January 29, 2021): 441–52. http://dx.doi.org/10.1177/0361198121990698.

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Bunching occurs when transit vehicles are unable to maintain their scheduled headways, resulting in two or more vehicles arriving at a stop in close succession and following each other too closely thereafter. Very few studies have explored the prediction of bunching in real-time, particularly for streetcar services. Predicting the time to bunching in real-time allows transit agencies to take more preventive actions to avoid the occurrence of bunching or to minimize its effects. In this study, we present a comprehensive literature review of the recent research conducted in bunching and real-time prediction models. Based on the findings from the literature review, we propose a model for real-time prediction of streetcar bunching. The Kalman filtering model predicts the travel time to bunching incidents and is tested and analyzed using an automatic vehicle location data feed for one streetcar route (Route 506 Carlton), obtained from the Toronto Transit Commission’s next bus system. The results show that: (1) the model provides good predication quality given that it relies only on the real-time GPS feed of streetcars, which makes it practical for use in real-time prediction applications; (2) the model prediction accuracy improves as the transit vehicle travels away from the terminal; and (3) increasing the number of past days involved in the calculations beyond 6 days or increasing the number of leading trips considered in the same day beyond 7 or 10 trips might increase the prediction error.

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11

Hong, Liang, and Ryan Martin. "Real-time Bayesian non-parametric prediction of solvency risk." Annals of Actuarial Science 13, no. 1 (February 7, 2018): 67–79. http://dx.doi.org/10.1017/s1748499518000039.

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AbstractInsurance regulation often dictates that insurers monitor their solvency risk in real time and take appropriate actions whenever the risk exceeds their tolerance level. Bayesian methods are appealing for prediction problems thanks to their ability to naturally incorporate both sample variability and parameter uncertainty into a predictive distribution. However, handling data arriving in real time requires a flexible non-parametric model, and the Monte Carlo methods necessary to evaluate the predictive distribution in such cases are not recursive and can be too expensive to rerun each time new data arrives. In this paper, we apply a recently developed alternative perspective on Bayesian prediction based on copulas. This approach facilitates recursive Bayesian prediction without computing a posterior, allowing insurers to perform real-time updating of risk measures to assess solvency risk, and providing them with a tool for carrying out dynamic risk management strategies in today’s “big data” era.

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Chien, Steven I. J., Xiaobo Liu, and Kaan Ozbay. "Predicting Travel Times for the South Jersey Real-Time Motorist Information System." Transportation Research Record: Journal of the Transportation Research Board 1855, no. 1 (January 2003): 32–40. http://dx.doi.org/10.3141/1855-04.

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A dynamic travel-time prediction model was developed for the South Jersey (southern New Jersey) motorist real-time information system. During development and evaluation of the model, the integration of traffic flow theory, measurement and application of collected data, and traffic simulation were considered. Reliable prediction results can be generated with limited historical real-time traffic data. In the study, acoustic sensors were installed at potential congested places to monitor traffic congestion. A developed simulation model was calibrated with the data collected from the sensors, and this was applied to emulate traffic operations and evaluate the proposed prediction model under time-varying traffic conditions. With emulated real–time information (travel times) generated by the simulation model, an algorithm based on Kalman filtering was developed and applied to forecast travel times for specific origin-destination pairs over different periods. Prediction accuracy was evaluated by the simulation model. Results show that the developed travel-time predictive model demonstrates satisfactory performance.

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13

Sobolev, Stephan, Andrey Babeyko, Rongjiang Wang, Roman Galas, Markus Rothacher, Jorn Lauterjung, Dmitry Sein, Jens Schröter, and Cecep Subarya. "Towards real-time tsunami amplitude prediction." Eos, Transactions American Geophysical Union 87, no. 37 (2006): 374. http://dx.doi.org/10.1029/2006eo370003.

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14

Asri, Hiba, Hajar Mousannif, and Hassan Al Moatassime. "Real-time Miscarriage Prediction with SPARK." Procedia Computer Science 113 (2017): 423–28. http://dx.doi.org/10.1016/j.procs.2017.08.272.

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15

Cotton, William R., Gregory Thompson, and Paul W. Mieike. "Real-Time Mesoscale Prediction on workstations." Bulletin of the American Meteorological Society 75, no. 3 (March 1994): 349–62. http://dx.doi.org/10.1175/1520-0477(1994)075<0349:rtmpow>2.0.co;2.

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Wang, Jinjun, Wei Xu, and Yihong Gong. "Real-time driving danger-level prediction." Engineering Applications of Artificial Intelligence 23, no. 8 (December 2010): 1247–54. http://dx.doi.org/10.1016/j.engappai.2010.01.001.

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Li, Haiguang, Zhao Li, Robert T. White, and Xindong Wu. "A real-time transportation prediction system." Applied Intelligence 39, no. 4 (January 19, 2013): 793–804. http://dx.doi.org/10.1007/s10489-012-0409-1.

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18

Liu, Zhi Cheng. "Real Time Prediction Method of Sensor Output Time Series." Advanced Materials Research 912-914 (April 2014): 1322–26. http://dx.doi.org/10.4028/www.scientific.net/amr.912-914.1322.

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In order to improve the real time prediction precision of sensor output time series, the predictable inner mechanism of time series is analyzed, and a method using wavelet filtering and neural network is proposed. Sensor output time series are first handled with wavelet filtering, and then predicted by neural network method. The proposed method can eliminate effect of measurement noise on prediction precision. Simulation experiment shows a higher prediction precision by the method. A new idea is given to increase prediction precision of sensor output time series by neural network-based methods.

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19

Xu, Tian-dong, Yuan Hao, Zhong-ren Peng, and Li-jun Sun. "Real-time travel time predictor for route guidance consistent with driver behavior." Canadian Journal of Civil Engineering 39, no. 10 (October 2012): 1113–24. http://dx.doi.org/10.1139/l2012-092.

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Providing reliable real-time travel time information is a critical challenge to all existing traffic routing systems. This study develops a new model for estimating and predicting real-time traffic conditions and travel times for variable message signs-based route guidance system. The proposed model is based on real-time limited detected traffic data, stochastic nonlinear macroscopic traffic flow model, and adaptive Kalman filtering theory. The method has the following main features: (1) real-time estimation and prediction of traffic conditions on a network level using limited traffic detectors, (2) travel time prediction in free flow and congested flow, and (3) prediction of drivers’ en-route diversion behavior. Field testing is conducted based on the Route Guidance Pilot Project sponsored by the National Science and Technology Ministry of China. The achieved testing results are satisfactory and have potential use for future works and field applications.

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20

Chen, L. Gwen, Adam Hartman, Brad Pugh, Jon Gottschalck, and David Miskus. "Real-Time Prediction of Areas Susceptible to Flash Drought Development." Atmosphere 11, no. 10 (October 17, 2020): 1114. http://dx.doi.org/10.3390/atmos11101114.

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Flash drought is a type of drought that develops quickly (usually within 2–4 weeks) in contrast to conventional, slowly evolving drought. Due to its sudden onset, flash drought is more difficult to predict and can cause major agricultural losses if it is not forecasted in a timely manner. To improve our ability to predict flash drought, we develop a subseasonal tool to predict areas susceptible to flash drought development using the Phase 2 of the North American Land Data Assimilation System (NLDAS-2) data. The tool calculates the rapid change index (RCI) using 7-day mean evapotranspiration anomalies. RCI is the accumulated magnitude of moisture stress changes (standardized differences) occurring over multiple weeks, and drought is likely to develop when RCI is negative. Since RCI changes with time, like all drought variables, it is difficult to capture drought development signals by monitoring RCI maps. In order to create an intuitive drought prediction map that directly depicts drought tendency, we use a threshold method to identify grid points with large decreases of 7-day mean evapotranspiration anomaly (i.e., RCI less than −0.5) in the last 30 days and under the condition that 3-month standardized precipitation index is less than −0.4. The real-time tool started running on 1 April 2018 at the NOAA Climate Prediction Center (CPC) and has been used to support CPC’s Monthly Drought Outlook efforts. The performance of the tool is evaluated using both retrospective and real-time predictions. The assessment shows promising results in predicting potential flash drought development, and the interplay between precipitation and high temperatures appears to be a challenge for flash drought prediction.

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21

Tang, Xiangyun, Dongliang Liao, Weijie Huang, Jin Xu, Liehuang Zhu, and Meng Shen. "Fully Exploiting Cascade Graphs for Real-time Forwarding Prediction." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 1 (May 18, 2021): 582–90. http://dx.doi.org/10.1609/aaai.v35i1.16137.

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Real-time forwarding prediction for predicting online contents' popularity is beneficial to various social applications for enhancing interactive social behaviors. Cascade graphs, formed by online contents' propagation, play a vital role in real-time forwarding prediction. Existing cascade graph modeling methods are inadequate to embed cascade graphs that have hub structures and deep cascade paths, or they fail to handle the short-term outbreak of forwarding amount. To this end, we propose a novel real-time forwarding prediction method that includes an effective approach for cascade graph embedding and a short-term variation sensitive method for time-series modeling, making the best of cascade graph features. Using two real world datasets, we demonstrate the significant superiority of the proposed method compared with the state-of-the-art. Our experiments also reveal interesting implications hidden in the performance differences between cascade graph embedding and time-series modeling.

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22

Malkin, Zinovy. "On Estimate of Real Accuracy of EOP Prediction." International Astronomical Union Colloquium 178 (2000): 505–10. http://dx.doi.org/10.1017/s0252921100061674.

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AbstractTo estimate the real accuracy of EOP predictions, real-time predictions made by the IERS Subbureau for Rapid Service and Prediction (USNO) and at the IAA EOP Service are analyzed. Methods of estimating prediction accuracy are discussed.

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23

Takahashi, Narumi, Kentaro Imai, Masanobu Ishibashi, Kentaro Sueki, Ryoko Obayashi, Tatsuo Tanabe, Fumiyasu Tamazawa, Toshitaka Baba, and Yoshiyuki Kaneda. "Real-Time Tsunami Prediction System Using DONET." Journal of Disaster Research 12, no. 4 (July 28, 2017): 766–74. http://dx.doi.org/10.20965/jdr.2017.p0766.

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We constructed a real-time tsunami prediction system using the Dense Oceanfloor Network System for Earthquakes and Tsunamis (DONET). This system predicts the arrival time of a tsunami, the maximum tsunami height, and the inundation area around coastal target points by extracting the proper fault models from 1,506 models based on the principle of tsunami amplification. Since DONET2, installed in the Nankai earthquake rupture zone, was constructed in 2016, it has been used in addition to DONET1 installed in the Tonankai earthquake rupture zone; we revised the system using both DONET1 and DONET2 to improve the accuracy of tsunami prediction. We introduced a few methods to improve the prediction accuracy. One is the selection of proper fault models from the entire set of models considering the estimated direction of the hypocenter using seismic and tsunami data. Another is the dynamic selection of the proper DONET observatories: only DONET observatories located between the prediction point and tsunami source are used for prediction. Last is preparation for the linked occurrence of double tsunamis with a time-lag. We describe the real-time tsunami prediction system using DONET and its implementation for the Shikoku area.

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Ma, Jiaman, Jeffrey Chan, Goce Ristanoski, Sutharshan Rajasegarar, and Christopher Leckie. "Bus travel time prediction with real-time traffic information." Transportation Research Part C: Emerging Technologies 105 (August 2019): 536–49. http://dx.doi.org/10.1016/j.trc.2019.06.008.

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25

Zhang, Kaimeng, Chi Tim Ng, and Myung Hwan Na. "Real time prediction of irregular periodic time series data." Journal of Forecasting 39, no. 3 (January 6, 2020): 501–11. http://dx.doi.org/10.1002/for.2637.

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26

Peña Fernández, Youssef, Heeren, Matthys, and Aerts. "Real-Time Model Predictive Control of Human Bodyweight Based on Energy Intake." Applied Sciences 9, no. 13 (June 27, 2019): 2609. http://dx.doi.org/10.3390/app9132609.

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The number of overweight people reached 1.9 billion in 2016. Lifespan decrease and many diseases have been linked to obesity. Efficient ways to monitor and control body weight are needed. The objective of this work is to explore the use of a model predictive control approach to manage bodyweight in response to energy intake. The analysis is performed based on data obtained during the Minnesota starvation experiment, with weekly measurements on body weight and energy intake for 32 male participants over the course of 27 weeks. A first order dynamic auto-regression with exogenous variables model exhibits the best prediction, with an average mean relative prediction error value of 1.01 ± 0.02% for 1 week-ahead predictions. Then, the performance of a model predictive control algorithm, following a predefined bodyweight trajectory, is tested. Root mean square errors of 0.30 ± 0.06 kg and 9 ± 3 kcal day-1 are found between the desired target and simulated bodyweights, and between the measured energy intake and advised by the controller energy intake, respectively. The model predictive control approach for bodyweight allows calculating the needed energy intake in order to follow a predefined target bodyweight reference trajectory. This study shows a first possible step towards real-time active control of human bodyweight.

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Barnes, Sean, Eric Hamrock, Matthew Toerper, Sauleh Siddiqui, and Scott Levin. "Real-time prediction of inpatient length of stay for discharge prioritization." Journal of the American Medical Informatics Association 23, e1 (August 7, 2015): e2-e10. http://dx.doi.org/10.1093/jamia/ocv106.

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Abstract Objective Hospitals are challenged to provide timely patient care while maintaining high resource utilization. This has prompted hospital initiatives to increase patient flow and minimize nonvalue added care time. Real-time demand capacity management (RTDC) is one such initiative whereby clinicians convene each morning to predict patients able to leave the same day and prioritize their remaining tasks for early discharge. Our objective is to automate and improve these discharge predictions by applying supervised machine learning methods to readily available health information. Materials and Methods The authors use supervised machine learning methods to predict patients’ likelihood of discharge by 2 p.m. and by midnight each day for an inpatient medical unit. Using data collected over 8000 patient stays and 20 000 patient days, the predictive performance of the model is compared to clinicians using sensitivity, specificity, Youden’s Index (i.e., sensitivity + specificity – 1), and aggregate accuracy measures. Results The model compared to clinician predictions demonstrated significantly higher sensitivity ( P < .01), lower specificity ( P < .01), and a comparable Youden Index ( P > .10). Early discharges were less predictable than midnight discharges. The model was more accurate than clinicians in predicting the total number of daily discharges and capable of ranking patients closest to future discharge. Conclusions There is potential to use readily available health information to predict daily patient discharges with accuracies comparable to clinician predictions. This approach may be used to automate and support daily RTDC predictions aimed at improving patient flow.

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Divya, A., and S. Lavanya. "Real Time Dengue Prediction Using Machine Learning." Indian Journal of Public Health Research & Development 11, no. 2 (February 1, 2020): 406. http://dx.doi.org/10.37506/v11/i2/2020/ijphrd/194834.

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Nuli, Sadguna, Nagula Vikranth, and Kothuri Avinash Gupta. "Real-Time Traffic Prediction Using Neural Networks." IOP Conference Series: Earth and Environmental Science 1086, no. 1 (September 1, 2022): 012029. http://dx.doi.org/10.1088/1755-1315/1086/1/012029.

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Abstract Somewhat recently, the issue of traffic has become more extreme because of industrialization particularly in significant urban communities. A higher standard of living in cities is pushing people to utilise their own vehicles instead of public transportation. Thus, there is tremendous increase in traffic, which leads to a variety of other associated issues. In order to get from one place to another, one must spend more time travelling than they would have otherwise, which also raises the fuel consumption. The traffic data that is gathered by various detectors is highly dynamic and non-stationary. For example, the number of vehicles turning at an intersection cannot be stated with greater accuracy. Consequently, it is challenging to develop a mathematical model to determine green time. However, the introduction of Intelligent Transportation Systems (ITS) in modern times enables the detection of traffic events, communication, information processing, and user action. One of the most crucial criteria for this system to work successfully is the ability to precisely predict the pattern of the traffic stream. In order to predict future traffic flow, a system that uses artificial neural networks and real-time traffic data was proposed in this study. Neural networks have the ability to predict the future by learning from the past. Specifically, this study predict traffic volume in two levels such as short-term for every 15 minutes interval and mid-term for every one hour of a given day. These predictions help in dynamic traffic management applications like signal control, congestion management, and travel time predictions etc.

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Mariescu-Istodor, Radu, Roxana Ungureanu, and Pasi Fränti. "Real-time destination prediction for mobile users." Advances in Cartography and GIScience of the ICA 2 (November 6, 2019): 1–7. http://dx.doi.org/10.5194/ica-adv-2-10-2019.

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Abstract. The number of GPS trajectories recorded daily has been continuously growing in the recent years and new methods to analyse such big data are surfacing all the time. In this paper, we focus on destination prediction, which is useful in various applications like hazard detection and advertisement. We proposed a real-time method for destination prediction of moving users. It uses the current movement trajectory of the user together with historical and regional information to make an accurate prediction. The method is efficient because we can rapidly compute features with the help of spatial and non-spatial indexing methods. We tested the method with real trajectories collected by Mopsi users. The success rate of the method is up to 65 % depending on the length of the recorded trajectory so far, i.e. how long the user has been on move. To our knowledge, this is the first real-time system capable of such success.

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Couceiro, R., P. Carvalho, R. P. Paiva, J. Muehlsteff, J. Henriques, C. Eickholt, C. Brinkmeyer, M. Kelm, and C. Meyer. "Real-Time Prediction of Neurally Mediated Syncope." IEEE Journal of Biomedical and Health Informatics 20, no. 2 (March 2016): 508–20. http://dx.doi.org/10.1109/jbhi.2015.2408994.

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Acharya, Sayandeep, Arjun Rajasekar, Barry S. Shender, Leonid Hrebien, and Moshe Kam. "Real-Time Hypoxia Prediction Using Decision Fusion." IEEE Journal of Biomedical and Health Informatics 21, no. 3 (May 2017): 696–707. http://dx.doi.org/10.1109/jbhi.2016.2528887.

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INOUE, Takuya, Tsuyoshi NAKATANI, and Hiroki YABE. "ROUTE SEARCH WITH REAL-TIME FLOOD PREDICTION." Journal of Japan Society of Civil Engineers, Ser. B1 (Hydraulic Engineering) 74, no. 4 (2018): I_1291—I_1296. http://dx.doi.org/10.2208/jscejhe.74.i_1291.

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Kang, Michael A., Matthew M. Churpek, Frank J. Zadravecz, Richa Adhikari, Nicole M. Twu, and Dana P. Edelson. "Real-Time Risk Prediction on the Wards." Critical Care Medicine 44, no. 8 (August 2016): 1468–73. http://dx.doi.org/10.1097/ccm.0000000000001716.

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35

Lu, S., H. Lu, and W. J. Kolarik. "Multivariate performance reliability prediction in real-time." Reliability Engineering & System Safety 72, no. 1 (April 2001): 39–45. http://dx.doi.org/10.1016/s0951-8320(00)00102-2.

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36

Shu, Min, and Wei Zhu. "Real-time prediction of Bitcoin bubble crashes." Physica A: Statistical Mechanics and its Applications 548 (June 2020): 124477. http://dx.doi.org/10.1016/j.physa.2020.124477.

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Abdel-Aty, Mohamed A., Hany M. Hassan, Mohamed Ahmed, and Ali S. Al-Ghamdi. "Real-time prediction of visibility related crashes." Transportation Research Part C: Emerging Technologies 24 (October 2012): 288–98. http://dx.doi.org/10.1016/j.trc.2012.04.001.

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Ni, Li-Feng, Ai-Qun Li, Fu-Yi Liu, Honore Yin, and J. R. Wu. "Real-time modeling prediction for excavation behavior." Structural Engineering and Mechanics 16, no. 6 (December 25, 2003): 643–54. http://dx.doi.org/10.12989/sem.2003.16.6.643.

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Khairdoost, Nima, Mohsen Shirpour, Michael A. Bauer, and Steven S. Beauchemin. "Real-Time Driver Maneuver Prediction Using LSTM." IEEE Transactions on Intelligent Vehicles 5, no. 4 (December 2020): 714–24. http://dx.doi.org/10.1109/tiv.2020.3003889.

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WU, Chao, Mian CHEN, and Yan JIN. "Real-time prediction method of borehole stability." Petroleum Exploration and Development 35, no. 1 (February 2008): 80–84. http://dx.doi.org/10.1016/s1876-3804(08)60012-9.

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Bhatia, Munish, Tariq Ahamed Ahanger, and Ankush Manocha. "Artificial intelligence based real-time earthquake prediction." Engineering Applications of Artificial Intelligence 120 (April 2023): 105856. http://dx.doi.org/10.1016/j.engappai.2023.105856.

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Moore, D., and A. Sawant. "TH-AB-303-03: Real-Time Error Estimation for Real-Time Motion Prediction." Medical Physics 42, no. 6 (June 2015): 3711. http://dx.doi.org/10.1118/1.4926158.

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Okamura, Kiyoshi, and Hiroyuki Sasahara. "0511 Cutting Edge Temperature Prediction on Low-frequency Vibration Drilling Considering Real Cutting Time." Proceedings of International Conference on Leading Edge Manufacturing in 21st century : LEM21 2015.8 (2015): _0511–1_—_0511–6_. http://dx.doi.org/10.1299/jsmelem.2015.8._0511-1_.

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Du, Cui Feng, Wen Ming Shen, and Shi Bao Jiang. "A Micro Regional Market Share Real-Time Prediction Based on Extended Kalman." Advanced Materials Research 846-847 (November 2013): 475–78. http://dx.doi.org/10.4028/www.scientific.net/amr.846-847.475.

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The real-time prediction of micro regional market share provides decision for the analysis of micro regional marketing scheme and micro regional channel planning. More and more increasing complexion mobile network environment require real-time micro area of market share and only mastering micro regional market share can have a more comprehensive understanding of market. To solve this problem, consideration of advantages of real-time aspects of the extended Kalman filtering algorithm in predicting, we propose a real-time prediction algorithm based on the extended Kalman filter Market Share. The algorithm can be real-time prediction of mobile network market share of base station. The simulation results show that the proposed algorithm in this paper is a real-time and good prediction quality.

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Prasad, Abhnil Amtesh, and Merlinde Kay. "Prediction of Solar Power Using Near-Real Time Satellite Data." Energies 14, no. 18 (September 16, 2021): 5865. http://dx.doi.org/10.3390/en14185865.

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Solar energy production is affected by the attenuation of incoming irradiance from underlying clouds. Often, improvements in the short-term predictability of irradiance using satellite irradiance models can assist grid operators in managing intermittent solar-generated electricity. In this paper, we develop and test a satellite irradiance model with short-term prediction capabilities using cloud motion vectors. Near-real time visible images from Himawari-8 satellite are used to derive cloud motion vectors using optical flow estimation techniques. The cloud motion vectors are used for the advection of pixels at future time horizons for predictions of irradiance at the surface. Firstly, the pixels are converted to cloud index using the historical satellite data accounting for clear, cloudy and cloud shadow pixels. Secondly, the cloud index is mapped to the clear sky index using a historical fitting function from the respective sites. Thirdly, the predicated all-sky irradiance is derived by scaling the clear sky irradiance with a clear sky index. Finally, a power conversion model trained at each site converts irradiance to power. The prediction of solar power tested at four sites in Australia using a one-month benchmark period with 5 min ahead prediction showed that errors were less than 10% at almost 34–60% of predicted times, decreasing to 18–26% of times under live predictions, but it outperformed persistence by >50% of the days with errors <10% for all sites. Results show that increased latency in satellite images and errors resulting from the conversion of cloud index to irradiance and power can significantly affect the forecasts.

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Shaji, Hima Elsa, Arun K. Tangirala, and Lelitha Vanajakshi. "Joint clustering and prediction approach for travel time prediction." PLOS ONE 17, no. 9 (September 23, 2022): e0275030. http://dx.doi.org/10.1371/journal.pone.0275030.

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Modeling and prediction of traffic systems is a challenging task due to the complex interactions within the system. Identification of significant regressors and using them to improve travel time predictions is a concept of interest. In previous studies, such regressors were identified offline and were static in nature. In this study, an iterative joint clustering and prediction approach is proposed to accurately predict spatiotemporal patterns in travel time. The clustering module is tied to the prediction module, and a prediction model is trained on each cluster. The combined clustering and prediction are then iterated until a chosen metric is optimized. This orients clusters of data towards prediction while enabling model development on subsets of travel time data with similar prediction complexity. The clusters created using the joint clustering and prediction approach confirmed to the real-world traffic scenario, forming clusters of high travel time at busy intersections and bus stops across the study stretch and forming clusters of low travel time in the sub-urban areas of the city. Further, a comparison of the developed framework with base methods demonstrated a decrease in prediction errors by at least 22.83%. This indicates that creating clusters of data that are sensitive to the quality of predictions using the joint clustering and prediction framework improves the accuracy of travel time predictions. The study also proposes criteria for choosing the best predictions when cluster-based predictions are used.

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Schwartz, Craig S., Glen S. Romine, Ryan A. Sobash, Kathryn R. Fossell, and Morris L. Weisman. "NCAR’s Experimental Real-Time Convection-Allowing Ensemble Prediction System." Weather and Forecasting 30, no. 6 (November 23, 2015): 1645–54. http://dx.doi.org/10.1175/waf-d-15-0103.1.

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Abstract This expository paper documents an experimental, real-time, 10-member, 3-km, convection-allowing ensemble prediction system (EPS) developed at the National Center for Atmospheric Research (NCAR) in spring 2015. The EPS is particularly unique in that continuously cycling, limited-area, mesoscale ensemble Kalman filter analyses provide diverse initial conditions. In addition to describing the EPS configurations, initial forecast assessments are presented that suggest the EPS can provide valuable severe weather guidance and skillful predictions of precipitation. The EPS output is available to operational forecasters, many of whom have incorporated the products into their toolboxes. Given such rapid embrace of an experimental system by the operational community, acceleration of convection-allowing EPS development is encouraged.

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Mendieta, Matias, and Hamed Tabkhi. "CARPe Posterum: A Convolutional Approach for Real-Time Pedestrian Path Prediction." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 3 (May 18, 2021): 2346–54. http://dx.doi.org/10.1609/aaai.v35i3.16335.

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Pedestrian path prediction is an essential topic in computer vision and video understanding. Having insight into the movement of pedestrians is crucial for ensuring safe operation in a variety of applications including autonomous vehicles, social robots, and environmental monitoring. Current works in this area utilize complex generative or recurrent methods to capture many possible futures. However, despite the inherent real-time nature of predicting future paths, little work has been done to explore accurate and computationally efficient approaches for this task. To this end, we propose a convolutional approach for real-time pedestrian path prediction, CARPe. It utilizes a variation of Graph Isomorphism Networks in combination with an agile convolutional neural network design to form a fast and accurate path prediction approach. Notable results in both inference speed and prediction accuracy are achieved, improving FPS considerably in comparison to current state-of-the-art methods while delivering competitive accuracy on well-known path prediction datasets.

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Majidpour, Mostafa, Hamidreza Nazaripouya, Peter Chu, Hemanshu Pota, and Rajit Gadh. "Fast Univariate Time Series Prediction of Solar Power for Real-Time Control of Energy Storage System." Forecasting 1, no. 1 (September 17, 2018): 107–20. http://dx.doi.org/10.3390/forecast1010008.

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In this paper, super-short-term prediction of solar power generation for applications in dynamic control of energy system has been investigated. In order to follow and satisfy the dynamics of the controller, the deployed prediction method should have a fast response time. To this end, this paper proposes fast prediction methods to provide the control system with one step ahead of solar power generation. The proposed methods are based on univariate time series prediction. That is, instead of using external data such as the weather forecast as the input of prediction algorithms, they solely rely on past values of solar power data, hence lowering the volume and acquisition time of input data. In addition, the selected algorithms are able to generate the forecast output in less than a second. The proposed methods in this paper are grounded on four well-known prediction algorithms including Autoregressive Integrated Moving Average (ARIMA), K-Nearest Neighbors (kNN), Support Vector Regression (SVR), and Random Forest (RF). The speed and accuracy of the proposed algorithms have been compared based on two different error measures, Mean Absolute Error (MAE) and Symmetric Mean Absolute Percentage Error (SMAPE). Real world data collected from the PV installation at the University of California, Riverside (UCR) are used for prediction purposes. The results show that kNN and RF have better predicting performance with respect to SMAPE and MAE criteria.

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Cluckie, I. D., A. Lane, and J. Yuan. "Modelling large urban drainage systems in real time." Water Science and Technology 39, no. 4 (February 1, 1999): 21–28. http://dx.doi.org/10.2166/wst.1999.0185.

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The interactions between rainfall and urban drainage systems (UDSs) are complex and must be considered as a whole in order to maximise control efficiency whilst at the same time achieving environmentally acceptable solutions. More rigorous standards, as a result of recent EU and UK legislation, are increasingly encouraging intervention in system management rather than more traditional passive procedures. To achieve these goals a global predictive real-time control (RTC) strategy is required, in which real-time flow prediction plays an important part in the provision of necessary first-hand information on system status in both current and predictive modes. This paper describes one such strategy, which differs from existing methods in the following ways: the novel way in which the UDS is represented; the algorithm used for model parameter identification; the strategies associated with the system output prediction; and the transfer function model used to represent the system. This transfer function model is a conceptually parameterised transfer function (CPTF) model, which by its nature falls into the category of lumped, dynamic, linear and conceptual although its structure takes the form of a non-conceptual transfer function model. The modelling approach is described as the RHINOS (Real-time urban Hydrological INfrastructure and Output modelling Strategy).

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