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A LOSS BOUND MODEL FOR ONLINE STOCHASTIC prediction algorithms

INFORMATION AND COMPUTATION 1995年第1期119卷 39-54页

作者： YAMANISHI, K NEC CORP LTD C&C INFORMAT TECHNOL RES LABSKAWASAKIKANAGAWA 213JAPAN

In this paper, we consider the problem of on-line prediction in which at each time an unlabeled instance is given and then a prediction algorithm outputs a probability distribution over the set of labels rather than {0, 1}-values before it sees the correct label. For this setting, we propose a weighted-average-type on-line stochastic prediction algorithm WA, which can be regarded as a hybrid of the Bayes algorithm and a sequential real-valued parameter estimation method. We derive upper bounds on the instantaneous logarithmic loss and cumulative logarithmic loss for WA in both the example-dependent form and the expected form (the expectation is taken with respect to the fixed target distribution of sequences). Specifically, under some specific parametric assumptions for target rules, we prove that WA is optimal in the sense that the upper bound on the expected cumulative logarithmic loss for WA asymptotically matches Rissanen's coding-theoretic lower bound. Further, we derive an upper bound on the expected cumulative quadratic loss by making use of relationships between the quadratic loss and the logarithmic loss. Throughout the paper, we relate computational learning theory to information theory, most specifically, Rissanen's predictive minimum description length principle, by giving noiseless coding theoretic interpretations to the loss bounds. (C) 1995 Academic Press, Inc.

关键词： Complexity Information Criterion Probability distribution prediction algorithms Information Theory Complexity algorithms Logarithmic Upper bound Minimum description length

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Development and validation of prediction algorithms for major depressive episode in the general population

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JOURNAL OF AFFECTIVE DISORDERS 2013年第1期151卷 39-45页

作者： Wang, Jian Li Manuel, Douglas Williams, Jeanne Schmitz, Norbert Gilmour, Heather Patten, Scott MacQueen, Glenda Birney, Arden Univ Calgary Fac Med Dept Psychiat Calgary AB T2N 4Z6 Canada Univ Calgary Fac Med Dept Community Hlth Sci Calgary AB T2N 4Z6 Canada Univ Ottawa Fac Med Dept Clin Epidemiol Ottawa ON K1N 6N5 Canada Univ Calgary Fac Med Dept Community Hlth Sci Calgary AB T2N 1N4 Canada McGill Univ Fac Med Dept Psychiat Montreal PQ H3A 0G4 Canada STAT Canada Hlth Anal Div Ottawa ON K1A 0T6 Canada Univ Calgary Fac Med Dept Psychiat Calgary AB T2N 1N4 Canada

Background: To develop and validate sex specific prediction algorithms for 4-year risk of major depressive episode (MDE) using data from a population based longitudinal cohort. Methods: Household residents from 10 provinces were randomly recruited and interviewed by Statistics Canada. 10,601 participants who were aged 18 years and older and who did not meet the criteria for MDE in the 12 months prior to a baseline interview in 2000/01 were included in algorithm development;data horn 7902 participants who were aged 18 and older and who were free of MDE in 2004/05 were used for validation. Validation was also conducted in sub-populations that are of practice and policy importance. MDE was assessed using the World Health Organization's Composite International Diagnostic Interview (CIDI)-Short Form for Major Depression (CIDI-SFMD). Results: In the training data, the C statistics for algorithms in men was 0.7953 and was 0.7667 for algorithm in women. The algorithms had good predictive power and calibrated well in the development and validation data. Limitations: The data relied on self-report. MDE was assessed with CIDI-SFMD. It was not feasible to validate the algorithms in different populations from different countries. Conclusions: More studies are needed to further validate and refine these algorithms. However, the ability of a small number of easily assessed variables to predict MDE risk indicates that algorithms are a promising strategy for identifying individuals in need of enhanced monitoring and preventive interventions. Ultimately, application of algorithms may lead to increased personalization of treatment, and better clinical outcomes. (C) 2013 The Authors. Published by Elsevier B.V. All rights reserved

关键词： prediction algorithms Major depression Sex-specific Population based

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A taxonomy of web prediction algorithms

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EXPERT SYSTEMS WITH APPLICATIONS 2012年第9期39卷 8496-8502页

作者： Domenech, Josep de la Ossa, Bernardo Sahuquillo, Julio Gil, Jose A. Pont, Ana Univ Politecn Valencia Valencia 46022 Spain

Web prefetching techniques are an attractive solution to reduce the user-perceived latency. These techniques are driven by a prediction engine or algorithm that guesses following actions of web users. A large amount of prediction algorithms has been proposed since the first prefetching approach was published, although it is only over the last two or three years when they have begun to be successfully implemented in commercial products. These algorithms can be implemented in any element of the web architecture and can use a wide variety of information as input. This affects their structure, data system, computational resources and accuracy. The knowledge of the input information and the understanding of how it can be handled to make predictions can help to improve the design of current prediction engines, and consequently prefetching techniques. This paper analyzes fifty of the most relevant algorithms proposed along 15 years of prefetching research and proposes a taxonomy where the algorithms are classified according to the input data they use. For each group, the main advantages and shortcomings are highlighted. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Web prefetching prediction algorithms

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Heuristic and Statistical prediction algorithms Survey or Smart Environments

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JOURNAL OF INFORMATION PROCESSING SYSTEMS 2020年第5期16卷 1196-1213页

作者： Malik, Sehrish Ullah, Israr Kim, DoHyeun Lee, KyuTae Jeju Natl Univ Dept Comp Engn Jeju South Korea Kongju Natl Univ Div Informat & Commun Engn Gongju South Korea Virtual Univ Pakistan Dept Comp Sci Lahore Pakistan

There is a growing interest in the development of smart environments through predicting the behaviors of inhabitants of smart spaces in the recent past. Various smart services are deployed in modern smart cities to facilitate residents and city administration. prediction algorithms are broadly used in the smart fields in order to well equip the smart services for the future demands. Hence, an accurate prediction technology plays a vital role in the smart services. In this paper, we take out an extensive survey of smart spaces such as smart homes, smart farms and smart cars and smart applications such as smart health and smart energy. Our extensive survey is based on more than 400 articles and the final list of research studies included in this survey consist of 134 research papers selected using Google Scholar database for period of 2008 to 2018. In this survey, we highlight the role of prediction algorithms in each sub-domain of smart Internet of Things (IoT) environments. We also discuss the main algorithms which play pivotal role in a particular IoT subfield and effectiveness of these algorithms. The conducted survey provides an efficient way to analyze and have a quick understanding of state of the art work in the targeted domain. To the best of our knowledge, this is the very first survey paper on main categories of prediction algorithms covering statistical, heuristic and hybrid approaches for smart environments.

关键词： prediction algorithms Smart Cities Smart Service

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Comparison of energy intake prediction algorithms for systems powered by photovoltaic harvesters

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MICROELECTRONICS JOURNAL 2010年第11期41卷 766-777页

作者： Bergonzini, Carlo Brunelli, Davide Benini, Luca Univ Trento DISI I-38123 Povo Trento Italy Univ Bologna DEIS I-40136 Bologna Italy

Small size photovoltaic modules can harvest enough energy to power many personal devices and wireless sensor nodes. The prediction of solar energy intake is possible thanks to the periodical availability of the sunlight and its cyclic behavior. Thus, smart and innovative power management strategies can take advantage from intake prediction algorithms to optimize the energy usage by keeping the system in low power state as long as possible. On the other hand, very accurate predictions need time and energy because of complex calculations, thus an algorithm that can provide the optimal trade-off between computational effort and accuracy is a breakthrough for systems with tight power constraints. In this paper we introduce an innovative, efficient and reliable solar prediction algorithm, the weather conditioned moving average (WCMA). The algorithm has been further enhanced to increase performance using a phase displacement regulator (PDR) which reduces the average error to less than 9.2% at a minimum energy cost. The proposed new algorithm compares favorably with several competing approaches. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Photovoltaic systems prediction algorithms Energy harvesting Power management Wireless sensor network

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Predicting web server crashes: A case study in comparing prediction algorithms

Predicting web server crashes: A case study in comparing pre...

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5th International Conference on Autonomic and Autonomous Systems

作者： Alonso, Javier Torres, Jordi Gavalda, Ricard Tech Univ Catalonia Comp Architecture Dept Barcelona Supercomp Ctr Barcelona Spain

ISBN: (纸本)9781424436842

Traditionally, performance has been the most important metries when evaluating a system. However, in the last decades industry and academia have been paying increasing attention to another metric to evaluate servers: availability. A web server may serve many users when running, but if it is out of service too much time, it becomes useless and expensive. The industry has adopted several techniques to improve system availability, yet crashes still happen. In this paper, we propose a new framework to predict time-to-failure when the system is suffering transient failures that consume resources randomly. We study which machine learning algorithms build a more accurate model of the behavior of the anomaly system, and focus on Linear Regression and Decision Tree algorithms. Our preliminary results show that M5P (a Decision Tree algorithm) is the best option to model the behavior of the system under the random injection of memory leaks.

关键词： Dependability High-Availability prediction algorithms Machine Learning

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Comparison of energy intake prediction algorithms for systems powered by photovoltaic harvesters

Comparison of energy intake prediction algorithms for system...

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3rd International Workshop on Advances in Sensors and Interfaces

作者： Bergonzini, Carlo Brunelli, Davide Benini, Luca Univ Trento DISI I-38123 Povo Trento Italy Univ Bologna DEIS I-40136 Bologna Italy

关键词： Photovoltaic systems prediction algorithms Energy harvesting Power management Wireless sensor network

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prediction algorithms using specialized software tools for steel industry equipment

Prediction algorithms using specialized software tools for s...

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International Symposium for Design and Technology of Electronics Packages (SIITME)

作者： E. Raducan V. Nicolau M. Andrei G. Petrea G.M. Vlej “Dunarea de Jos” University of Galati Romania Liberty Steel Group Galati Romania

ISBN: (数字)9781728175065

ISBN: (纸本)9781728175072

The paper aims to present a model for predictive maintenance applicable in steel industry for critic equipment. This paper investigates the application of multi-step time prediction to sustain the turbo blowers (TB) equipment prognostics using software analytics algorithms which describe forecasting models, statistical approach or the formulas specifies for industrial equipment developed based by DAX formulas. This application represent a new method for realize a predictive maintenance describe as an industrial revolution characterized by smart systems and Internet-based solutions.

关键词： Maintenance engineering Predictive models Atmospheric modeling Predictive maintenance Forecasting Steel industry prediction algorithms

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prediction algorithms for Real-Time Variable-Bit-Rate Video

Prediction Algorithms for Real-Time Variable-Bit-Rate Video

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Asia-Pacific Conference on Communications

作者： Huabing Liu Guoqiang Mao School of Electrical and Information Engineering University of Sydney Australia

ISBN: (纸本)9780780391321

Accurate prediction of variable bit rate (VBR) video traffic can be used to improve the network utilization efficiency while supporting guaranteed QoS requirements of VBR video. On-line prediction algorithms have been proposed in the literature to forecast real-time VBR video traffic for dynamic bandwidth allocation. In this paper, we survey a number of algorithms both in time domain and wavelet domain for video traffic prediction. The features of the existing algorithms are summarized, and on the basis of it we propose a time-domain and a wavelet-domain normalized least mean square (NLMS) based adaptive prediction scheme respectively. Our proposed time-domain scheme combines the separation and differential techniques in the literature to reduce short-term bit rate variation of VBR video traffic and smooth the data for more accurate perdition. Our proposed wavelet-domain prediction scheme uses the a trous wavelet transform instead of conventional decimated wavelet transform to improve the prediction accuracy by exploiting the redundant information in the wavelet transform coefficients. Simulations using three half-an-hour long full-motion moving picture experts group (MPEG) video traces show that our proposed methods can achieve better performance than those in the literature

关键词： prediction algorithms Telecommunication traffic Wavelet transforms Bit rate Time domain analysis Channel allocation Wavelet domain Traffic control Accuracy MPEG standards

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Environmental performance evaluation of electric enterprises during a power crisis: Evidence from DEA methods and AI prediction algorithms

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ENERGY ECONOMICS 2024年 130卷

作者： Pan, Yinghao Zhang, Chao -Chao Lee, Chien -Chiang Lv, Suxiang Univ Sci & Technol China Sch Management Hefei Peoples R China Nanchang Univ Sch Econ & Management Nanchang Peoples R China Lebanese Amer Univ Adnan Kassar Sch Business Beirut Lebanon Nanchang Univ Res Ctr Cent China Econ & Social Dev Nanchang Peoples R China Beijing Technol & Business Univ Sch Econ Beijing Peoples R China

With the continuous occurrence of power crisis events worldwide, meeting society's demand for electricity has kept coal-fired power generation high, which leads to a large amount of carbon dioxide emissions and environmental pollution problems. Therefore, improving the environmental performance of thermal power plants under the background of a power crisis has become particularly important. This research provides a new path for production optimization and environmental performance improvement of electric power enterprises from the perspective of combining prediction algorithms and DEA methods. We apply the modified non -radial directional distance function (MNDDF) method to a set of panel data containing 16 different cogeneration units and 27 different periods in eastern China and construct the grey prediction model to predict the future environmental performance of these units. Empirical analysis shows that our model has high prediction accuracy and offers a reasonable basis for future production decision -making adjustments for electric power enterprises. Based on the results herein, we present valuable suggestions for thermal power plants to improve their environmental performance during a power market crisis, including optimizing resource utilization, improving management level and operating conditions, strengthening technological innovation, and enhancing production factor allocation.

关键词： Power market crisis Electric power enterprises Environmental performance prediction algorithms Grey prediction model Artificial intelligence

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