1,322 research outputs found

    Forecasting Long-Term Government Bond Yields: An Application of Statistical and AI Models

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    This paper evaluates several artificial intelligence and classical algorithms on their ability of forecasting the monthly yield of the US 10-year Treasury bonds from a set of four economic indicators. Due to the complexity of the prediction problem, the task represents a challenging test for the algorithms under evaluation. At the same time, the study is of particular significance for the important and paradigmatic role played by the US market in the world economy. Four data-driven artificial intelligence approaches are considered, namely, a manually built fuzzy logic model, a machine learned fuzzy logic model, a self-organising map model and a multi-layer perceptron model. Their performance is compared with the performance of two classical approaches, namely, a statistical ARIMA model and an econometric error correction model. The algorithms are evaluated on a complete series of end-month US 10-year Treasury bonds yields and economic indicators from 1986:1 to 2004:12. In terms of prediction accuracy and reliability of the modelling procedure, the best results are obtained by the three parametric regression algorithms, namely the econometric, the statistical and the multi-layer perceptron model. Due to the sparseness of the learning data samples, the manual and the automatic fuzzy logic approaches fail to follow with adequate precision the range of variations of the US 10-year Treasury bonds. For similar reasons, the self-organising map model gives an unsatisfactory performance. Analysis of the results indicates that the econometric model has a slight edge over the statistical and the multi-layer perceptron models. This suggests that pure data-driven induction may not fully capture the complicated mechanisms ruling the changes in interest rates. Overall, the prediction accuracy of the best models is only marginally better than the prediction accuracy of a basic one-step lag predictor. This result highlights the difficulty of the modelling task and, in general, the difficulty of building reliable predictors for financial markets.interest rates; forecasting; neural networks; fuzzy logic.

    A Fraud-Detection Fuzzy Logic Based System for the Sudanese Financial Sector

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    Financial fraud considered as a global issue that faces the financial sector and economy; as a result, many financial institutions loose hundreds of millions of dollars annually due to fraud. In Sudan, there are difficulties of getting real data from banks and the unavailability of systems which explain the reasons of suspicious transaction. Hence, there is a need for transparent techniques which can automatically detect fraud with high accuracy and identify its causes and common patterns. Some of the Artificial Intelligence (AI) techniques provide good predictive models, nevertheless they are considered as black-box models which are not easy to understand and analyze. In this paper, we developed a novel intelligent type-2 Fuzzy Logic Systems (FLSs) which can detect fraud in debit cards using real world dataset extracted from financial institutions in Sudan. FLSs provide white-box transparent models which employ linguistic labels and IF-Then rules which could be easily analyzed, interpreted and augmented by the fraud experts. The proposed type-2 FLS system learnt its fuzzy sets parameters from data using Fuzzy C-means (FCM) clustering as well as learning the FLS rules from data. The proposed system has the potential to result in highly accurate automatic fraud-detection for the Sudanese financial institutions and banking sectors

    A Big-Bang Big-Crunch Type-2 Fuzzy Logic-based System for Malaria Epidemic Prediction in Ethiopia

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    ABSTRACT- Malaria is a life-threatening disease caused by Plasmodium parasite infection with huge medical, economic, and social impact. Malaria is one of a serious public health problem in Ethiopia since 1959, even if, its morbidity and mortality have been reduced starting from 2001. Various studies were conducted to predict the Malaria epidemic using mathematical and statistical regression approaches, nevertheless, they had no learning capabilities. In this paper, we presented a type-2 fuzzy logic-based system for Malaria epidemic prediction (MEP) in Ethiopia which has been optimized by the Big-Bang Big-Crunch (BBBC) approach to maximizing the model accuracy and interpretability to predict for the future occurrence of Malaria. We compared the proposed BBBC optimized type-2 fuzzy logic-based system against its counterpart T1FLS, non-optimized T2FLS, ANFIS and ANN. The results show that the optimized proposed T2FLS provides a more interpretable model that predicts the future occurrence of Malaria from one up to three months ahead with optimal accuracy. This helps to answer the question of when and where must make preparation to prevent and control the occurrence of Malaria epidemic since the generated rules from our system were able to explain the situations and intensity of input factors which contributed to the Malaria epidemic and outbreak

    AI and OR in management of operations: history and trends

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    The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested

    Multiobjective Evolutionary Optimization of Type-2 Fuzzy Rule-Based Systems for Financial Data Classification

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    Classification techniques are becoming essential in the financial world for reducing risks and possible disasters. Managers are interested in not only high accuracy, but in interpretability and transparency as well. It is widely accepted now that the comprehension of how inputs and outputs are related to each other is crucial for taking operative and strategic decisions. Furthermore, inputs are often affected by contextual factors and characterized by a high level of uncertainty. In addition, financial data are usually highly skewed toward the majority class. With the aim of achieving high accuracies, preserving the interpretability, and managing uncertain and unbalanced data, this paper presents a novel method to deal with financial data classification by adopting type-2 fuzzy rule-based classifiers (FRBCs) generated from data by a multiobjective evolutionary algorithm (MOEA). The classifiers employ an approach, denoted as scaled dominance, for defining rule weights in such a way to help minority classes to be correctly classified. In particular, we have extended PAES-RCS, an MOEA-based approach to learn concurrently the rule and data bases of FRBCs, for managing both interval type-2 fuzzy sets and unbalanced datasets. To the best of our knowledge, this is the first work that generates type-2 FRBCs by concurrently maximizing accuracy and minimizing the number of rules and the rule length with the objective of producing interpretable models of real-world skewed and incomplete financial datasets. The rule bases are generated by exploiting a rule and condition selection (RCS) approach, which selects a reduced number of rules from a heuristically generated rule base and a reduced number of conditions for each selected rule during the evolutionary process. The weight associated with each rule is scaled by the scaled dominance approach on the fuzzy frequency of the output class, in order to give a higher weight to the minority class. As regards the data base learning, the membership function parameters of the interval type-2 fuzzy sets used in the rules are learned concurrently to the application of RCS. Unbalanced datasets are managed by using, in addition to complexity, selectivity and specificity as objectives of the MOEA rather than only the classification rate. We tested our approach, named IT2-PAES-RCS, on 11 financial datasets and compared our results with the ones obtained by the original PAES-RCS with three objectives and with and without scaled dominance, the FRBCs, fuzzy association rule-based classification model for high-dimensional dataset (FARC-HD) and fuzzy unordered rules induction algorithm (FURIA), the classical C4.5 decision tree algorithm, and its cost-sensitive version. Using nonparametric statistical tests, we will show that IT2-PAES-RCS generates FRBCs with, on average, accuracy statistically comparable with and complexity lower than the ones generated by the two versions of the original PAES-RCS. Further, the FRBCs generated by FARC-HD and FURIA and the decision trees computed by C4.5 and its cost-sensitive version, despite the highest complexity, result to be less accurate than the FRBCs generated by IT2-PAES-RCS. Finally, we will highlight how these FRBCs are easily interpretable by showing and discussing one of them

    A Type-2 Fuzzy Logic Based System for Malaria Epidemic Prediction in Ethiopia

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    Malaria is the most prevalent mosquito-borne disease throughout tropical and subtropical regions of the world with severe medical, economic, and social impact. Malaria is a serious public health problem in Ethiopia since 1959, even if, its morbidity and mortality have been reduced starting from 2001. Various studies were conducted to predict the malaria epidemic using mathematical and statistical approaches, nevertheless, they had no learning capabilities. In this paper, we present a Type-2 Fuzzy Logic Based System for Malaria epidemic prediction in Ethiopia which was trained using real data collected throughout Ethiopia from 2013 to 2017. Fuzzy Logic Based Systems provide a transparent model which employs IF-Then rules for the prediction that could be easily analyzed and interpreted by decision-makers. This is quite important to fight the sources of Malaria and take the needed preventive measures where the generated rules from our system were able to explain the situations and intensity of input factors which contributed to Malaria epidemic incidence up to three months ahead. The presented Type-2 Fuzzy Logic System (T2FLS) learns its rules and fuzzy set parameters from data and was able to outperform its counterparts T1FLS in 2% and ANFIS in 0.33% in the accuracy of prediction of Malaria epidemic in Ethiopia. In addition, the proposed system did shed light on the main causes behind such outbreaks in Ethiopia because of its high level of interpretabilit

    Designing an occupancy flow-based controller for airport terminals

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    One of the most cost-effective ways to save energy in commercial buildings is through designing a dedicated controller for adjusting environmental set-points according occupancy flow. This paper presents the design of a fuzzy rule-based supervisory controller for reducing energy consumptions while simultaneously providing comfort for passengers in a large airport terminal building. The inputs to the controller are the time schedule of the arrival and departure of passenger planes as well as the expected number of passengers, zone global illuminance (daylight) and external temperature. The outputs from the controller are optimised temperature, airflow and lighting set-point profiles for the building. The supervisory controller was designed based on expert knowledge in MATLAB/Simulink, and then validated using simulation studies. The simulation results demonstrate significant potential for energy savings in the controller's ability to maintain comfort by adjusting set-points according to the flow of passengers. Practical application : The systematic approach adopted here, including the use of artificial intelligence to design supervisory controllers, can be extended to other large buildings which have variable but predictable occupancy patterns like the restricted area of the airport terminal building

    Forecasting long-term government bond yields: an application of statistical and ai models

    Get PDF
    This paper evaluates several artificial intelligence and classical algorithms on their ability of forecasting the monthly yield of the US 10-year Treasury bonds from a set of four economic indicators. Due to the complexity of the prediction problem, the task represents a challenging test for the algorithms under evaluation. At the same time, the study is of particular significance for the important and paradigmatic role played by the US market in the world economy. Four data-driven artificial intelligence approaches are considered, namely, a manually built fuzzy logic model, a machine learned fuzzy logic model, a self-organising map model and a multi-layer perceptron model. Their performance is compared with the performance of two classical approaches, namely, a statistical ARIMA model and an econometric error correction model. The algorithms are evaluated on a complete series of end-month US 10-year Treasury bonds yields and economic indicators from 1986:1 to 2004:12. In terms of prediction accuracy and reliability of the modelling procedure, the best results are obtained by the three parametric regression algorithms, namely the econometric, the statistical and the multi-layer perceptron model. Due to the sparseness of the learning data samples, the manual and the automatic fuzzy logic approaches fail to follow with adequate precision the range of variations of the US 10-year Treasury bonds. For similar reasons, the self-organising map model gives an unsatisfactory performance. Analysis of the results indicates that the econometric model has a slight edge over the statistical and the multi-layer perceptron models. This suggests that pure data-driven induction may not fully capture the complicated mechanisms ruling the changes in interest rates. Overall, the prediction accuracy of the best models is only marginally better than the prediction accuracy of a basic one-step lag predictor. This result highlights the difficulty of the modelling task and, in general, the difficulty of building reliable predictors for financial markets

    Modeling and Optimal Design of Machining-Induced Residual Stresses in Aluminium Alloys Using a Fast Hierarchical Multiobjective Optimization Algorithm

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    The residual stresses induced during shaping and machining play an important role in determining the integrity and durability of metal components. An important issue of producing safety critical components is to find the machining parameters that create compressive surface stresses or minimise tensile surface stresses. In this paper, a systematic data-driven fuzzy modelling methodology is proposed, which allows constructing transparent fuzzy models considering both accuracy and interpretability attributes of fuzzy systems. The new method employs a hierarchical optimisation structure to improve the modelling efficiency, where two learning mechanisms cooperate together: NSGA-II is used to improve the model’s structure while the gradient descent method is used to optimise the numerical parameters. This hybrid approach is then successfully applied to the problem that concerns the prediction of machining induced residual stresses in aerospace aluminium alloys. Based on the developed reliable prediction models, NSGA-II is further applied to the multi-objective optimal design of aluminium alloys in a ‘reverse-engineering’ fashion. It is revealed that the optimal machining regimes to minimise the residual stress and the machining cost simultaneously can be successfully located

    Global plant characterisation and distribution with evolution and climate

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    Since Arrhenius published seminal work in 1921, research interest in the description of plant traits and grouped characteristics of plant species has grown, underpinning diversity in trophic levels. Geographic exploration and diversity studies prior to and after 1921 culminated in biological, chemical and computer-simulated approaches describing rudiments of growth patterns within dynamic conditions of Earth. This thesis has two parts:- classical theory and multidisciplinary fusion to give mathematical strength to characterising plant species in space and time.Individual plant species occurrences are used to obtain a Species-Area Relationship. The use of both Boolean and logic-based mathematics is then integrated to describe classical methods and propose fuzzy logic control to predict species ordination. Having demonstrated a lack of significance between species and area for data modelled in this thesis a logic based approach is taken. Mamdani and T-S-K fuzzy system stability is verified by application to individual plant occurrences, validated by a multiple interfaced data portal. Quantitative mathematical models are differentiated with a genetic programming approach, enabling visualisation of multi-objective dispersal of plant strategies, plant metabolism and life-forms within the water-energy dynamic of a fixed time-scale scenario. The distributions of plant characteristics are functionally enriched through the use of Gaussian process models. A generic framework of a Geographic Information System is used to visualise distributions and it is noted that such systems can be used to assist in design and implementation of policies. The study has made use of field based data and the application of mathematic methods is shown to be appropriate and generative in the description of characteristics of plant species, with the aim of application of plant strategies, life-forms and photosynthetic types to a global framework. Novel application of fuzzy logic and related mathematic method to plant distribution and characteristics has been shown on a global scale. Quantification of the uncertainty gives novel insight through consequent trophic levels of biological systems, with great relevance to mathematic and geographic subject development. Informative value of Z matrices of plant distribution is increased substantiating sustainability and conservation policy value to ecosystems and human populations dependent upon them for their needs.Key words: sustainability, conservation policy, Boolean and logic-based, fuzzy logic, genetic programming, multi-objective dispersal, strategies, metabolism, life-forms
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