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

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.

Artificial Intelligence Applied to Project Success: A Literature Review

Project control and monitoring tools are based on expert judgement and parametric tools. Projects are the means by which companies implement their strategies. However project success rates are still very low. This is a worrying situation that has a great economic impact so alternative tools for project success prediction must be proposed in order to estimate project success or identify critical factors of success. Some of these tools are based on Artificial Intelligence. In this paper we will carry out a literature review of those papers that use Artificial Intelligence as a tool for project success estimation or critical success factor identification

A Review on the Application of Natural Computing in Environmental Informatics

Natural computing offers new opportunities to understand, model and analyze the complexity of the physical and human-created environment. This paper examines the application of natural computing in environmental informatics, by investigating related work in this research field. Various nature-inspired techniques are presented, which have been employed to solve different relevant problems. Advantages and disadvantages of these techniques are discussed, together with analysis of how natural computing is generally used in environmental research.Comment: Proc. of EnviroInfo 201

An agent-based fuzzy cognitive map approach to the strategic marketing planning for industrial firms

This is the post-print version of the final paper published in Industrial Marketing Management. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2013 Elsevier B.V.Industrial marketing planning is a typical example of an unstructured decision making problem due to the large number of variables to consider and the uncertainty imposed on those variables. Although abundant studies identified barriers and facilitators of effective industrial marketing planning in practice, the literature still lacks practical tools and methods that marketing managers can use for the task. This paper applies fuzzy cognitive maps (FCM) to industrial marketing planning. In particular, agent based inference method is proposed to overcome dynamic relationships, time lags, and reusability issues of FCM evaluation. MACOM simulator also is developed to help marketing managers conduct what-if scenarios to see the impacts of possible changes on the variables defined in an FCM that represents industrial marketing planning problem. The simulator is applied to an industrial marketing planning problem for a global software service company in South Korea. This study has practical implication as it supports marketing managers for industrial marketing planning that has large number of variables and their cause–effect relationships. It also contributes to FCM theory by providing an agent based method for the inference of FCM. Finally, MACOM also provides academics in the industrial marketing management discipline with a tool for developing and pre-verifying a conceptual model based on qualitative knowledge of marketing practitioners.Ministry of Education, Science and Technology (Korea

Modeling Financial Time Series with Artificial Neural Networks

Financial time series convey the decisions and actions of a population of human actors over time. Econometric and regressive models have been developed in the past decades for analyzing these time series. More recently, biologically inspired artificial neural network models have been shown to overcome some of the main challenges of traditional techniques by better exploiting the non-linear, non-stationary, and oscillatory nature of noisy, chaotic human interactions. This review paper explores the options, benefits, and weaknesses of the various forms of artificial neural networks as compared with regression techniques in the field of financial time series analysis.CELEST, a National Science Foundation Science of Learning Center (SBE-0354378); SyNAPSE program of the Defense Advanced Research Project Agency (HR001109-03-0001

A Soft Computing Approach to Dynamic Load Balancing in 3GPP LTE

A major objective of the 3GPP LTE standard is the provision of high-speed data services. These services must be guaranteed under varying radio propagation conditions, to stochastically distributed mobile users. A necessity for determining and regulating the traffic load of eNodeBs naturally ensues. Load balancing is a self-optimization operation of self-organizing networks (SON). It aims at ensuring an equitable distribution of users in the network. This translates into better user satisfaction and a more efficient use of network resources. Several methods for load balancing have been proposed. Most of the algorithms are based on hard (traditional) computing which does not utilize the tolerance for precision of load balancing. This paper proposes the use of soft computing, precisely adaptive Neuro-fuzzy inference system (ANFIS) model for dynamic QoS aware load balancing in 3GPP LTE. The use of ANFIS offers learning capability of neural network and knowledge representation of fuzzy logic for a load balancing solution that is cost effective and closer to human intuitio

Big data-driven fuzzy cognitive map for prioritising IT service procurement in the public sector

YesThe prevalence of big data is starting to spread across the public and private sectors however, an impediment to its widespread adoption orientates around a lack of appropriate big data analytics (BDA) and resulting skills to exploit the full potential of big data availability. In this paper, we propose a novel BDA to contribute towards this void, using a fuzzy cognitive map (FCM) approach that will enhance decision-making thus prioritising IT service procurement in the public sector. This is achieved through the development of decision models that capture the strengths of both data analytics and the established intuitive qualitative approach. By taking advantages of both data analytics and FCM, the proposed approach captures the strength of data-driven decision-making and intuitive model-driven decision modelling. This approach is then validated through a decision-making case regarding IT service procurement in public sector, which is the fundamental step of IT infrastructure supply for publics in a regional government in the Russia federation. The analysis result for the given decision-making problem is then evaluated by decision makers and e-government expertise to confirm the applicability of the proposed BDA. In doing so, demonstrating the value of this approach in contributing towards robust public decision-making regarding IT service procurement.EU FP7 project Policy Compass (Project No. 612133

Artificial Intelligence Enabled Project Management: A Systematic Literature Review

In the Industry 5.0 era, companies are leveraging the potential of cutting-edge technologies such as artificial intelligence for more efficient and green human-centric production. In a similar approach, project management would benefit from artificial intelligence in order to achieve project goals by improving project performance, and consequently, reaching higher sustainable success. In this context, this paper examines the role of artificial intelligence in emerging project management through a systematic literature review; the applications of AI techniques in the project management performance domains are presented. The results show that the number of influential publications on artificial intelligence-enabled project management has increased significantly over the last decade. The findings indicate that artificial intelligence, predominantly machine learning, can be considerably useful in the management of construction and IT projects; it is notably encouraging for enhancing the planning, measurement, and uncertainty performance domains by providing promising forecasting and decision-making capabilities