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Guided Fast Local Search for Speeding Up a Financial Forecasting Algorithm

Guided Fast Local Search for Speeding Up a Financial Forecas...

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IEEE Conference on Computational Intelligence for Financial Engineering and Economics (CIFEr)

作者： Shao, Ming Smonou, Dafni Kampouridis, Michael Tsang, Edward Univ Essex Ctr Computat Finance & Econ Agents Colchester CO4 3SQ Essex England Univ Kent Sch Comp Canterbury ME4 4AG Kent England

ISBN: (纸本)9781479923809

Guided Local Search is a powerful meta-heuristic algorithm that has been applied to a successful Genetic Programming Financial Forecasting tool called EDDIE. Although previous research has shown that it has significantly improved the performance of EDDIE, it also increased its computational cost to a high extent. This paper presents an attempt to deal with this issue by combining Guided Local Search with Fast Local Search, an algorithm that has shown in the past to be able to significantly reduce the computational cost of Guided Local Search. Results show that EDDIE's computational cost has been reduced by an impressive 77%, while at the same time there is no cost to the predictive performance of the algorithm.

关键词： Algorithmdesignandanalysis Computationalefficiency Equations Forecasting Mathematicalmodel predictionalgorithms Radiofrequency radiofrequency Speeding algorithms Searches Equation

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FAULT PROGNOSIS FOR ROTATING ELECTRICAL MACHINES MONITORING USING RECURSIVE LEAST SQUARE 6

FAULT PROGNOSIS FOR ROTATING ELECTRICAL MACHINES MONITORING ...

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6th European Embedded Design in Education and Research Conference (EDERC)

作者： Rocchi, M. Mosciaro, F. Grottesi, F. Scortichini, M. Giantomassi, A. Pirro, M. Grisostomi, M. Ippoliti, G. Univ Politecn Marche Dipartimento Ingn Informaz Via Brecce Bianche I-60131 Ancona Italy

ISBN: (纸本)9781479968435

Nowadays there is a strong competition between companies in order to develop new solutions to keep competitive on the market, e.g. by reducing the production costs. Since the maintenance costs are substantial share of the production costs, companies need to plan the maintenance costs effectively. Deterioration prognosis of electric machine can decrease the maintenance costs by minimizing the loss of production due to machine breakdown and avoiding the overstocking of spare parts. This paper proposes a prognostic system to predict the wear condition of rotating electrical machines. Experimentations have been carried out on a laboratory experimental system, which is equipped with analog-to-digital converters, a microcontroller and two accelerometers in order to acquire vibration measurements and predict the machine degradation trend. Tests are carried out on induction motors using an electrical motor test bench to show the performances.

关键词： Acceleration Electricmotors Faultdetection Marketresearch predictionalgorithms Prognosticsandhealthmanagement Vibrations Rotating machines Maintenance costs Vibration acceleration Companies electric machines recursive least squares vibration monitoring Microcontrollers Production cost induction motors Least squares method

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Purchase Likelihood Prediction for Targeted Organic Food Marketing Campaigns in China

Purchase Likelihood Prediction for Targeted Organic Food Mar...

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Portland International Conference on Management of Engineering & Technology (PICMET)

作者： Giannini, Beau Chen, Song Paramonov, Pavel Wu, Ying Yu Tongji Univ Sch Econ & Management Shanghai 200092 Peoples R China Multidisciplinary Insights LLC San Francisco CA 94108 USA

ISBN: (纸本)9781890843298

The demand for organic food products in China is growing in response to both increased spending power and food safety concerns. However, identifying likely buyers of organic products proves challenging due to their relatively small fraction in the overall population. Our study explores applications of machine learning algorithms for effective management of organic food marketing campaigns in China. Based on the data we collected through an online choice-experiment type questionnaire of Chinese consumers, a purchase likelihood estimation framework has been developed that utilizes customer profile traits such as age group, family status, education level, and geographic location. In addition, we apply clustering techniques to perform data-driven organic market segmentation and identify consumer profiles ready to pay more for high quality, certified organic products. The resulting market segments are compared to various types of organic consumers discussed in the literature. Our algorithms provide a useful framework for online retailers who are seeking lean strategies of market entry in China with their health food brands.

关键词： Certification Citiesandtowns Dairyproducts Economicindicators Education Machinelearningalgorithms predictionalgorithms certification organic product Education Organic foods Food Machine learning algorithms Geographical Locations Consumers Telemarketing Likelihood MACHINE APPLICATIONS

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Dynamic Frequency Scaling for Energy Consumption Reduction in Synchronous Distributed Applications 12

Dynamic Frequency Scaling for Energy Consumption Reduction i...

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12th IEEE International Symposium on Parallel and Distributed Processing with Applications (ISPA)

作者： Charr, Jean-Claude Couturier, Raphael Fanfakh, Ahmed Giersch, Arnaud Univ Franche Comte FEMTO ST Inst IUT Belfort Montbeliard F-90016 Belfort France

ISBN: (纸本)9781479942930

Dynamic Voltage Frequency Scaling (DVFS) can be applied to modern CPUs. This technique is usually used to reduce the energy consumed by a CPU while computing. Thus, decreasing the frequency reduces the power consumed by the CPU. However, it can also significantly affect the performance of the executed program if it is compute bound and if a low CPU frequency is selected. Therefore, the chosen scaling factor must give the best possible trade-off between energy reduction and performance. In this paper we present an algorithm that predicts the energy consumed with each frequency gear and selects the one that gives the best ratio between energy consumption reduction and performance. This algorithm works online without training or profiling and has a very small overhead. It also takes into account synchronous communications between the nodes that are executing the distributed algorithm. The algorithm has been evaluated over the SimGrid simulator while being applied to the NAS parallel benchmark programs. The results of the experiments show that it outperforms other existing scaling factor selection algorithms.

关键词： Benchmarktesting Degradation Energyconsumption predictionalgorithms Programprocessors Time-frequencyanalysis Dynamicvoltagefrequencyscaling homogeneouscluster messagepassingprograms central processing units energy consumption Energy sources dynamic frequency deterioration Scale factor Energy conservation

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Joint Voting Prediction for Questions and Answers in CQA

Joint Voting Prediction for Questions and Answers in CQA

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IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)

作者： Yao, Yuan Tong, Hanghang Xie, Tao Akoglu, Leman Xu, Feng Lu, Jian Nanjing Univ State Key Lab Novel Software Technol Nanjing Jiangsu Peoples R China CUNY City Coll New York NY USA Univ Illinois Urbana IL 61801 USA SUNY Stony Brook Stony Brook NY USA

ISBN: (纸本)9781479958771

Community Question Answering (CQA) sites have become valuable repositories that host a massive volume of human knowledge. How can we detect a high-value answer which clears the doubts of many users? Can we tell the user if the question s/he is posting would attract a good answer? In this paper, we aim to answer these questions from the perspective of the voting outcome by the site users. Our key observation is that the voting score of an answer is strongly positively correlated with that of its question, and such correlation could be in turn used to boost the prediction performance. Armed with this observation, we propose a family of algorithms to jointly predict the voting scores of questions and answers soon after they are posted in the CQA sites. Experimental evaluations demonstrate the effectiveness of our approaches.

关键词： Conferences Correlation Educationalinstitutions Joints Knowledgediscovery Logistics predictionalgorithms Logistics Joints Joints Vote Answer Power grids Consumers

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A Study of Age and Gender seen through Mobile Phone Usage Patterns in Mexico

A Study of Age and Gender seen through Mobile Phone Usage Pa...

引用

IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)

作者： Sarraute, Carlos Blanc, Pablo Burroni, Javier Grandata Labs Buenos Aires DF Argentina UBA FCEN Dept Math Buenos Aires DF Argentina

ISBN: (纸本)9781479958771

Mobile phone usage provides a wealth of information, which can be used to better understand the demographic structure of a population. In this paper we focus on the population of Mexican mobile phone users. Our first contribution is an observational study of mobile phone usage according to gender and age groups. We were able to detect significant differences in phone usage among different subgroups of the population. Our second contribution is to provide a novel methodology to predict demographic features (namely age and gender) of unlabeled users by leveraging individual calling patterns, as well as the structure of the communication graph. We provide details of the methodology and show experimental results on a real world dataset that involves millions of users.

关键词： Algorithmdesignandanalysis Machinelearningalgorithms Mobilehandsets predictionalgorithms Socialnetworkservices Sociology Statistics

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Fast Computation of Look-Ahead Rao-Blackwellised Particle Filter in SLAM

Fast Computation of Look-Ahead Rao-Blackwellised Particle Fi...

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International Electrical Engineering Congress (iEECON)

作者： Yuvapoositanon, Peerapol Mahanakorn Univ Technol Dept Elect Engn Ctr Elect Syst Design & Signal Proc CESdSP Bangkok 10530 Thailand

ISBN: (纸本)9781479931743

In this paper, we explore a novel strategy for fast computation of the look-ahead Rao-Blackwellised Particle Filtering (la-RBPF) algorithm for the simultaneous localization and mappping (SLAM) problem in the probabilistic robotics framework. We show that the complexity of the existing algorithm can be substantially reduced by computing for the Kalman filtering prediction and update steps to only a representative particle of a group of particles offering the same robot's poses. Simulation results reveal the potential of the proposed method in reducing the computational time steps as compared to the original la-RBPF algorithm without affecting the performance. The test results also show its superior estimation accuracy as compared to the standard RBPF SLAM algorithm when the number of particles is small.

关键词： Filteringalgorithms predictionalgorithms Simultaneouslocalizationandmapping Yttrium supersonic low altitude missile Particle filters Rare earths yttrium arithmetic Fasting

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Combining Different Meta-heuristics to Improve the Predictability of a Financial Forecasting Algorithm

Combining Different Meta-heuristics to Improve the Predictab...

引用

IEEE Conference on Computational Intelligence for Financial Engineering and Economics (CIFEr)

作者： Aluko, Babatunde Smonou, Dafni Kampouridis, Michael Tsang, Edward Univ Essex Ctr Computat Finance & Econ Agents Colchester CO4 3SQ Essex England Univ Kent Sch Comp Chatham ME4 4AG Kent England

ISBN: (纸本)9781479923809

Hyper-heuristics have successfully been applied to a vast number of search and optimization problems. One of the novelties of hyper-heuristics is the fact that they manage and automate the meta-heuristic's selection process. In this paper, we implemented and analyzed a hyper-heuristic framework on three meta-heuristics namely Simulated Annealing, Tabu Search, and Guided Local Search, which had successfully been applied in the past to a Financial Forecasting algorithm called EDDIE. EDDIE uses Genetic Programming to extract and learn from historical data in order to predict future financial market movements. Results show that the algorithm's effectiveness has been improved, thus making the combination of meta-heuristics under a hyper-heuristic framework an effective Financial Forecasting approach.

关键词： Educationalinstitutions Finance Forecasting Genetics predictionalgorithms Radiofrequency Spaceexploration radiofrequency Genetics historical data financing Genetic Algorithm (GA)

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Enhancing Intraday Trading Performance of Neural Network using Dynamic Volatility Clustering Fuzzy Filter

Enhancing Intraday Trading Performance of Neural Network usi...

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IEEE Conference on Computational Intelligence for Financial Engineering and Economics (CIFEr)

作者： Vella, Vince Ng, Wing Lon Univ Essex Ctr Computat Finance & Econ Agents Wivenhoe Pk Colchester CO4 3SQ Essex England

ISBN: (纸本)9781479923809

We extend Neural Network (NN) trading models with an innovative and efficient volatility filter based on fuzzy c-means clustering algorithm, where the choice for the number of clusters, a frequent problem with cluster analysis, is selected by optimizing a global risk-return performance measure. Our algorithm automatically extracts fuzzy rules from past trades by taking into account the predicted return size and intraday time varying realized volatility, the latter used as a proxy for uncertainty. The model identifies unique intraday scenarios and subsequently creates a dynamic and visually apprehensible risk-return search space to control algorithmic trading decisions. Our results show that this method can be successfully applied to support high-frequency intraday trading strategies, outperforming both standard NN and buy-and-hold models.

关键词： Equations Heuristicalgorithms Marketresearch Mathematicalmodel predictionalgorithms Standards Uncertainty Neural network Volatility Fuzzy Uncertainty fuzzy filter Cluster Analysis Proxy Equation

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Predicting Equity Market Price Impact with Performance Weighted Ensembles of Random Forests

Predicting Equity Market Price Impact with Performance Weigh...

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IEEE Conference on Computational Intelligence for Financial Engineering and Economics (CIFEr)

作者： Booth, Ash Gerding, Enrico McGroarty, Frank Univ Southampton Inst Complex Syst Simulat Southampton SO9 5NH Hants England Univ Southampton Elect & Comp Sci Southampton SO9 5NH Hants England Univ Southampton Sch Management Southampton SO9 5NH Hants England

ISBN: (纸本)9781479923809

For many players in financial markets, the price impact of their trading activity represents a large proportion of their transaction costs. This paper proposes a novel machine learning method for predicting the price impact of order book events. Specifically, we introduce a prediction system based on performance weighted ensembles of random forests. The system's performance is benchmarked using ensembles of other popular regression algorithms including: liner regression, neural networks and support vector regression using depth-of-book data from the BATS Chi-X exchange. The results show that recency-weighted ensembles of random forests produce over 15% greater prediction accuracy on out-of-sample data, for 5 out of 6 timeframes studied, compared with all benchmarks.

关键词： Biologicalsystemmodeling Datamodels Mathematicalmodel predictionalgorithms Predictivemodels Training Vegetation Ensembles vegetation Functional training System performance Prices Random Data sampling Forest PREDICTION SYSTEM Training

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