Enhanced artificial bee colony-least squares support vector machines algorithm for time series prediction

Over the past decades, the Least Squares Support Vector Machines (LSSVM) has been widely utilized in prediction task of various application domains. Nevertheless, existing literature showed that the capability of LSSVM is highly dependent on the value of its hyper-parameters, namely regularization parameter and kernel parameter, where this would greatly affect the generalization of LSSVM in prediction task. This study proposed a hybrid algorithm, based on Artificial Bee Colony (ABC) and LSSVM, that consists of three algorithms; ABC-LSSVM, lvABC-LSSVM and cmABC-LSSVM. The lvABC algorithm is introduced to overcome the local optima problem by enriching the searching behaviour using Levy mutation. On the other hand, the cmABC algorithm that incorporates conventional mutation addresses the over- fitting or under-fitting problem. The combination of lvABC and cmABC algorithm, which is later introduced as Enhanced Artificial Bee Colony–Least Squares Support Vector Machine (eABC-LSSVM), is realized in prediction of non renewable natural resources commodity price. Upon the completion of data collection and data pre processing, the eABC-LSSVM algorithm is designed and developed. The predictability of eABC-LSSVM is measured based on five statistical metrics which include Mean Absolute Percentage Error (MAPE), prediction accuracy, symmetric MAPE (sMAPE), Root Mean Square Percentage Error (RMSPE) and Theils’ U. Results showed that the eABC-LSSVM possess lower prediction error rate as compared to eight hybridization models of LSSVM and Evolutionary Computation (EC) algorithms. In addition, the proposed algorithm is compared to single prediction techniques, namely, Support Vector Machines (SVM) and Back Propagation Neural Network (BPNN). In general, the eABC-LSSVM produced more than 90% prediction accuracy. This indicates that the proposed eABC-LSSVM is capable of solving optimization problem, specifically in the prediction task. The eABC-LSSVM is hoped to be useful to investors and commodities traders in planning their investment and projecting their profit

Asset allocation under regime-switching models

We discuss an optimal asset allocation problem in a wide class of discrete-time regime-switching models including the hidden Markovian regime-switching (HMRS) model, the interactive hidden Markovian regime-switching (IHMRS) model and the self-exciting threshold autoregressive (SETAR) model. In the optimal asset allocation problem, the object of the investor is to select an optimal portfolio strategy so as to maximize the expected utility of wealth over a finite investment horizon. We solve the optimal portfolio problem using a dynamic programming approach in a discrete-time set up. Numerical results are provided to illustrate the practical implementation of the models and the impacts of different types of regime switching on optimal portfolio strategies. © 2012 IEEE.published_or_final_versio

Value for Money Integration in the Renegotiation of Public Private Partnership Road Projects

The governments of various countries have continued to adopt Public Private Partnership (PPP) for infrastructure projects delivery due to its many advantages over the traditional procurement method. However, concerns have been raised by stakeholders about the viability of PPP to deliver Value for Money (VfM), especially for the client. These discussions have generated debates and arguments in policy and advisory documents within the last decade mainly in the renegotiation of PPP water and transport projects and their VfM implications. Poor or non-achievement of VfM in PPP contracts renegotiation has led to this study in PPP road projects with the overall aim of integrating VfM considerations into the renegotiation process of PPP road projects. Mixed methodology research approach is used to achieve the objectives set for the study. Interviews and questionnaires of professionals involved in Design-Build-Finance-Operate (DBFO) road projects in the UK are used in the study. The qualitative and quantitative analysis of the data collected revealed that technical, contractual and additional works are the categories of factors leading to renegotiations and have an impact on the achievement of VfM. These findings show that renegotiation does not necessarily have to erode the VfM benefits of PPP road projects for the client and lead to user’s dissatisfaction regarding quality, fees, and charges. The research shows that the very critical factors leading to the renegotiation of road concessions are changes to works standards, specifications, the scope of works, and additional works. The findings also indicate that design and planning measures such as clear and concise contract documents, a definition of detailed criteria for VfM and performance indicators, and accurate estimation of contract requirements amongst others are critical measures to ensure the achievement of VfM at the renegotiation of PFI (DBFO) road projects. Also, VfM can further be achieved for the renegotiations that are predominantly motivated by technical and contractual factors. This study developed a VfM renegotiation framework for the UK PFI (DBFO) road projects. The five constituents of the VfM renegotiation framework are identification and establishment of measures and mechanisms, the factors leading to renegotiation and their level of criticalities, impacts of the renegotiation on VfM criteria, the identification of renegotiation outcomes and their natures and the application of remedial actions (if necessary). The concept of the framework is premised on the importance of defining and agreeing on appropriate measures and VfM contractual mechanisms by both public and private stakeholders at the contract inception to guide future renegotiation. An assessment of the factors, impacts, and outcomes of the renegotiation is necessary during the stages of implementation of the PPP road projects to develop an understanding of the implications of the renegotiation on VfM. The knowledge of the impacts of renegotiations during implementation will inform the responsible stakeholder's decision on the appropriate actions required to address any observed deviations from the project performance indicators or value for money criteria defined at the inception of the contract. The public and private partners can achieve their respective VfM objectives while also achieving user’s satisfaction through the adoption of the proposed VfM renegotiation framework. There is, however, a need for the public and private partners who will be the primary beneficiary of the framework to be proactively involved in the use of the framework from contract inception to handing over of the project residual value to the client. The formulation of measures for renegotiation at the outset of the contract as indicated in the framework is essential to achieving VfM at renegotiation. Also, the client should ensure that flexibility is built into the agreement regarding the contract mechanisms for payment from the beginning, to allow either party to introduce proposals that can enhance the achievement of VfM at renegotiation or change negotiation