Photovoltaic power forecasting with a rough set combination method

One major challenge with integrating photovoltaic (PV) systems into the grid is that its power generation is intermittent and uncontrollable due to the variation in solar radiation. An accurate PV power forecasting is crucial to the safe operation of the grid connected PV power station. In this work, a combined model with three different PV forecasting models is proposed based on a rough set method. The combination weights for each individual model are determined by rough set method according to its significance degree of condition attribute. The three different forecasting models include a past-power persistence model, a support vector machine (SVM) model and a similar data prediction model. The case study results show that, in comparison with each single forecasting model, the proposed combined model can identify the amount of useful information in a more effective manner

Performance Forecasting of Share Market using Machine Learning Techniques: A Review

Forecasting share performance becomes more challenging issue due to the enormous amount of valuable trading data stored in the stock database. Currently, existing forecasting methods are insufficient to analyze the share performance accurately. There are two main reasons for that: First, the study of existing forecasting methods is still insufficient to identify the most suitable methods for share price prediction. Second, the lack of investigations made on the factors affecting the share performance. In this regard, this study presents a systematic review of the last fifteen years on various machine learning techniques in order to analyze share performance accurately. The only objective of this study is to provide an overview of the machine learning techniques that have been used to forecast share performance. This paper also highlights a how the prediction algorithms can be used to identify the most important variables in a share market dataset. Finally, we could have succeeded to analyze share performance effectively. It could bring benefits and impacts to researchers, society, brokers and financial analysts

Technical and Fundamental Features Analysis for Stock Market Prediction with Data Mining Methods

Predicting stock prices is an essential objective in the financial world. Forecasting stock returns and their risk represents one of the most critical concerns of market decision makers. This thesis investigates the stock price forecasting with three approaches from the data mining concept and shows how different elements in the stock price can help to enhance the accuracy of our prediction. For this reason, the first and second approaches capture many fundamental indicators from the stocks and implement them as explanatory variables to do stock price classification and forecasting. In the third approach, technical features from the candlestick representation of the share prices are extracted and used to enhance the accuracy of the forecasting. In each approach, different tools and techniques from data mining and machine learning are employed to justify why the forecasting is working. Furthermore, since the idea is to evaluate the potential of features in the stock trend forecasting, therefore we diversify our experiments using both technical and fundamental features. Therefore, in the first approach, a three-stage methodology is developed while in the first step, a comprehensive investigation of all possible features which can be effective on stocks risk and return are identified. Then, in the next stage, risk and return are predicted by applying data mining techniques for the given features. Finally, we develop a hybrid algorithm, based on some filters and function-based clustering; and re-predicted the risk and return of stocks. In the second approach, instead of using single classifiers, a fusion model is proposed based on the use of multiple diverse base classifiers that operate on a common input and a meta-classifier that learns from base classifiers’ outputs to obtain a more precise stock return and risk predictions. A set of diversity methods, including Bagging, Boosting, and AdaBoost, is applied to create diversity in classifier combinations. Moreover, the number and procedure for selecting base classifiers for fusion schemes are determined using a methodology based on dataset clustering and candidate classifiers’ accuracy. Finally, in the third approach, a novel forecasting model for stock markets based on the wrapper ANFIS (Adaptive Neural Fuzzy Inference System) – ICA (Imperialist Competitive Algorithm) and technical analysis of Japanese Candlestick is presented. Two approaches of Raw-based and Signal-based are devised to extract the model’s input variables and buy and sell signals are considered as output variables. To illustrate the methodologies, for the first and second approaches, Tehran Stock Exchange (TSE) data for the period from 2002 to 2012 are applied, while for the third approach, we used General Motors and Dow Jones indexes.Predicting stock prices is an essential objective in the financial world. Forecasting stock returns and their risk represents one of the most critical concerns of market decision makers. This thesis investigates the stock price forecasting with three approaches from the data mining concept and shows how different elements in the stock price can help to enhance the accuracy of our prediction. For this reason, the first and second approaches capture many fundamental indicators from the stocks and implement them as explanatory variables to do stock price classification and forecasting. In the third approach, technical features from the candlestick representation of the share prices are extracted and used to enhance the accuracy of the forecasting. In each approach, different tools and techniques from data mining and machine learning are employed to justify why the forecasting is working. Furthermore, since the idea is to evaluate the potential of features in the stock trend forecasting, therefore we diversify our experiments using both technical and fundamental features. Therefore, in the first approach, a three-stage methodology is developed while in the first step, a comprehensive investigation of all possible features which can be effective on stocks risk and return are identified. Then, in the next stage, risk and return are predicted by applying data mining techniques for the given features. Finally, we develop a hybrid algorithm, based on some filters and function-based clustering; and re-predicted the risk and return of stocks. In the second approach, instead of using single classifiers, a fusion model is proposed based on the use of multiple diverse base classifiers that operate on a common input and a meta-classifier that learns from base classifiers’ outputs to obtain a more precise stock return and risk predictions. A set of diversity methods, including Bagging, Boosting, and AdaBoost, is applied to create diversity in classifier combinations. Moreover, the number and procedure for selecting base classifiers for fusion schemes are determined using a methodology based on dataset clustering and candidate classifiers’ accuracy. Finally, in the third approach, a novel forecasting model for stock markets based on the wrapper ANFIS (Adaptive Neural Fuzzy Inference System) – ICA (Imperialist Competitive Algorithm) and technical analysis of Japanese Candlestick is presented. Two approaches of Raw-based and Signal-based are devised to extract the model’s input variables and buy and sell signals are considered as output variables. To illustrate the methodologies, for the first and second approaches, Tehran Stock Exchange (TSE) data for the period from 2002 to 2012 are applied, while for the third approach, we used General Motors and Dow Jones indexes.154 - Katedra financívyhově

Fusion of multiple diverse predictors in stock market

Forecasting stock returns and their risk represents one of the most important concerns of market decision makers. Although many studies have examined single classifiers of stock returns and risk methods, fusion methods, which have only recently emerged, require further study in this area. The main aim of this paper is to propose a fusion model based on the use of multiple diverse base classifiers that operate on a common input and a Meta classifier that learns from base classifiers’ outputs to obtain more precise stock return and risk predictions. A set of diversity methods, including Bagging, Boosting and AdaBoost, is applied to create diversity in classifier combinations. Moreover, the number and procedure for selecting base classifiers for fusion schemes is determined using a methodology based on dataset clustering and candidate classifiers’ accuracy. The results demonstrate that Bagging exhibited superior performance within the fusion scheme and could achieve a maximum of 83.6% accuracy with Decision Tree, LAD Tree and Rep Tree for return prediction and 88.2% accuracy with BF Tree, DTNB and LAD Tree in risk prediction. For feature selection part, a wrapper-GA algorithm is developed and compared with the fusion model. This paper seeks to help researcher select the best individual classifiers and fuse the proper scheme in stock market prediction. To illustrate the approach, we apply it to Tehran Stock Exchange (TSE) data for the period from 2002 to 2012

Essays on Predictive Analytics in E-Commerce

Die Motivation für diese Dissertation ist dualer Natur: Einerseits ist die Dissertation methodologisch orientiert und entwickelt neue statistische Ansätze und Algorithmen für maschinelles Lernen. Gleichzeitig ist sie praktisch orientiert und fokussiert sich auf den konkreten Anwendungsfall von Produktretouren im Onlinehandel. Die “data explosion”, veursacht durch die Tatsache, dass die Kosten für das Speichern und Prozessieren großer Datenmengen signifikant gesunken sind (Bhimani and Willcocks, 2014), und die neuen Technologien, die daraus resultieren, stellen die größte Diskontinuität für die betriebliche Praxis und betriebswirtschaftliche Forschung seit Entwicklung des Internets dar (Agarwal and Dhar, 2014). Insbesondere die Business Intelligence (BI) wurde als wichtiges Forschungsthema für Praktiker und Akademiker im Bereich der Wirtschaftsinformatik (WI) identifiziert (Chen et al., 2012). Maschinelles Lernen wurde erfolgreich auf eine Reihe von BI-Problemen angewandt, wie zum Beispiel Absatzprognose (Choi et al., 2014; Sun et al., 2008), Prognose von Windstromerzeugung (Wan et al., 2014), Prognose des Krankheitsverlaufs von Patienten eines Krankenhauses (Liu et al., 2015), Identifikation von Betrug Abbasi et al., 2012) oder Recommender-Systeme (Sahoo et al., 2012). Allerdings gibt es nur wenig Forschung, die sich mit Fragestellungen um maschinelles Lernen mit spezifischen Bezug zu BI befasst: Obwohl existierende Algorithmen teilweise modifiziert werden, um sie auf ein bestimmtes Problem anzupassen (Abbasi et al., 2010; Sahoo et al., 2012), beschränkt sich die WI-Forschung im Allgemeinen darauf, existierende Algorithmen, die für andere Fragestellungen als BI entwickelt wurden, auf BI-Fragestellungen anzuwenden (Abbasi et al., 2010; Sahoo et al., 2012). Das erste wichtige Ziel dieser Dissertation besteht darin, einen Beitrag dazu zu leisten, diese Lücke zu schließen. Diese Dissertation fokussiert sich auf das wichtige BI-Problem von Produktretouren im Onlinehandel für eine Illustration und praktische Anwendung der vorgeschlagenen Konzepte. Viele Onlinehändler sind nicht profitabel (Rigby, 2014) und Produktretouren sind eine wichtige Ursache für dieses Problem (Grewal et al., 2004). Neben Kostenaspekten sind Produktretouren aus ökologischer Sicht problematisch. In der Logistikforschung ist es weitestgehend Konsens, dass die “letzte Meile” der Zulieferkette, nämlich dann wenn das Produkt an die Haustür des Kunden geliefert wird, am CO2-intensivsten ist (Browne et al., 2008; Halldórsson et al., 2010; Song et al., 2009). Werden Produkte retourniert, wird dieser energieintensive Schritt wiederholt, wodurch sich die Nachhaltigkeit und Umweltfreundlichkeit des Geschäftsmodells von Onlinehändlern relativ zum klassischen Vertrieb reduziert. Allerdings können Onlinehändler Produktretouren nicht einfach verbieten, da sie einen wichtigen Teil ihres Geschäftsmodells darstellen: So hat die Möglichkeit, Produkte zu retournieren positive Auswirkungen auf Kundenzufriedenheit (Cassill, 1998), Kaufverhalten (Wood, 2001), künftiges Kaufverhalten (Petersen and Kumar, 2009) und emotianale Reaktionen der Kunden (Suwelack et al., 2011). Ein vielversprechender Ansatz besteht darin, sich auf impulsives und kompulsives (LaRose, 2001) sowie betrügerisches Kaufverhalten zu fokussieren (Speights and Hilinski, 2005; Wachter et al., 2012). In gegenwärtigen akademschen Literatur zu dem Thema gibt es keine solchen Strategien. Die meisten Strategien unterscheiden nicht zwischen gewollten und ungewollten Retouren (Walsh et al., 2014). Das zweite Ziel dieser Dissertation besteht daher darin, die Basis für eine Strategie von Prognose und Intervention zu entwickeln, mit welcher Konsumverhalten mit hoher Retourenwahrscheinlichkeit im Vorfeld erkannt und rechtzeitig interveniert werden kann. In dieser Dissertation werden mehrere Prognosemodelle entwickelt, auf Basis welcher demonstriert wird, dass die Strategie, unter der Annahme moderat effektiver Interventionsstrategien, erhebliche Kosteneinsparungen mit sich bringt

Early Warning System for Bankruptcy: Bankruptcy Prediction

The recent bankruptcies of large joint stock companies in U.S. and Europe shook investors across the world and underlined the importance of failure prediction both in academia and industry. It now seems more necessary than ever to develop early warning systems that can help to prevent or avert corporate default. These systems facilitate the selection of firms to collaborate with or invest in

Probabilistic modelling of oil rig drilling operations for business decision support: a real world application of Bayesian networks and computational intelligence.

This work investigates the use of evolved Bayesian networks learning algorithms based on computational intelligence meta-heuristic algorithms. These algorithms are applied to a new domain provided by the exclusive data, available to this project from an industry partnership with ODS-Petrodata, a business intelligence company in Aberdeen, Scotland. This research proposes statistical models that serve as a foundation for building a novel operational tool for forecasting the performance of rig drilling operations. A prototype for a tool able to forecast the future performance of a drilling operation is created using the obtained data, the statistical model and the experts' domain knowledge. This work makes the following contributions: applying K2GA and Bayesian networks to a real-world industry problem; developing a well-performing and adaptive solution to forecast oil drilling rig performance; using the knowledge of industry experts to guide the creation of competitive models; creating models able to forecast oil drilling rig performance consistently with nearly 80% forecast accuracy, using either logistic regression or Bayesian network learning using genetic algorithms; introducing the node juxtaposition analysis graph, which allows the visualisation of the frequency of nodes links appearing in a set of orderings, thereby providing new insights when analysing node ordering landscapes; exploring the correlation factors between model score and model predictive accuracy, and showing that the model score does not correlate with the predictive accuracy of the model; exploring a method for feature selection using multiple algorithms and drastically reducing the modelling time by multiple factors; proposing new fixed structure Bayesian network learning algorithms for node ordering search-space exploration. Finally, this work proposes real-world applications for the models based on current industry needs, such as recommender systems, an oil drilling rig selection tool, a user-ready rig performance forecasting software and rig scheduling tools

Agents in the market place an exploratory study on using intelligent agents to trade financial instruments

Tese de doutoramento em InformáticaThis dissertation documents our exploratory research aimed at investigating the utilization of intelligent agents in the development of automated financial trading strategies. In order to demonstrate this potential use for agent technology, we propose a hybrid cognitive architecture meant for the creation of autonomous agents capable of trading different types of financial instruments. This architecture was used to implement 10 currency trading agents and 25 stock trading agents. Their overall performance, evaluated according to the cumulative return and the maximum drawdown metrics, was found to be acceptable in a reasonably long simulation period. In order to improve this performance, we defined negotiation protocols that allowed the integration of the 35 trading agents in a multi-agent system, which proved to be better suited for withstanding sudden market events, due to the diversification of the investments. This system obtained very promising results, and remains open to many obvious improvements. Our findings lead us to conclude that there is indeed a place for intelligent agents in the financial industry; in particular, they hold the potential to be employed in the establishment of investment companies where software agents make all the trading decisions, with human intervention being relegated to simple administrative tasks.Esta dissertação documenta um estudo exploratório destinado a investigar a utilização de agentes inteligentes no desenvolvimento de estratégias de investimento financeiro automatizadas. Para demonstrar este uso potencial para tecnologia de agentes, foi proposta uma arquitectura cognitiva híbrida destinada à criação de agentes autónomos capazes de negociar diferentes tipos de instrumentos financeiros. Esta arquitectura foi utilizada para implementar 10 agentes que negoceiam pares cambiais, e 25 agentes que negoceiam acções. A performance global destes agentes, avaliada de acordo com as métricas de retorno acumulado e drawdown máximo, foi considerada aceitável ao longo de um período de simulação relativamente longo. Para melhorar esta performance, foram definidos protocolos de negociação que permitiram a integração dos 35 agentes num sistema multi-agente, que demonstrou estar melhor preparado para enfrentar alterações súbitas nos mercados, devido à diversificação dos investimentos. Este sistema obteve resultados muito promissores, e pode ainda ser sujeito a diversos melhoramentos. Os nossos resultados indiciam que os agentes inteligentes podem ocupar um lugar de relevo na indústria financeira; em particular, aparentam ter potencial suficiente para serem aplicados na criação de fundos de investimento onde todas as decisões de negociação são efectuadas por agentes de software, sendo a intervenção humana relegada para tarefas administrativas básicas

Recent Advances in Social Data and Artificial Intelligence 2019

The importance and usefulness of subjects and topics involving social data and artificial intelligence are becoming widely recognized. This book contains invited review, expository, and original research articles dealing with, and presenting state-of-the-art accounts pf, the recent advances in the subjects of social data and artificial intelligence, and potentially their links to Cyberspace