Forecasting exchange rates with linear and nonlinear models

In this paper, the exchange rate forecasting performance of neural network models are evaluated against the random walk, autoregressive moving average and generalised autoregressive conditional heteroskedasticity models. There are no guidelines available that can be used to choose the parameters of neural network models and therefore, the parameters are chosen according to what the researcher considers to be the best. Such an approach, however,implies that the risk of making bad decisions is extremely high, which could explain why in many studies, neural network models do not consistently perform better than their time series counterparts. In this paper, through extensive experimentation, the level of subjectivity in building neural network models is considerably reduced and therefore giving them a better chance of Forecasting exchange rates with linear and nonlinear models 415 performing well. The results show that in general, neural network models perform better than the traditionally used time series models in forecasting exchange rates.

A comparison of linear forecasting models and neural networks:an application to Euro inflation and Euro Divisia

Linear models reach their limitations in applications with nonlinearities in the data. In this paper new empirical evidence is provided on the relative Euro inflation forecasting performance of linear and non-linear models. The well established and widely used univariate ARIMA and multivariate VAR models are used as linear forecasting models whereas neural networks (NN) are used as non-linear forecasting models. It is endeavoured to keep the level of subjectivity in the NN building process to a minimum in an attempt to exploit the full potentials of the NN. It is also investigated whether the historically poor performance of the theoretically superior measure of the monetary services flow, Divisia, relative to the traditional Simple Sum measure could be attributed to a certain extent to the evaluation of these indices within a linear framework. Results obtained suggest that non-linear models provide better within-sample and out-of-sample forecasts and linear models are simply a subset of them. The Divisia index also outperforms the Simple Sum index when evaluated in a non-linear framework. © 2005 Taylor & Francis Group Ltd.

A note on DEA models in technology selection:an improvement of Karsak and Ahiska's approach

This paper introduces a compact form for the maximum value of the non-Archimedean in Data Envelopment Analysis (DEA) models applied for the technology selection, without the need to solve a linear programming (LP). Using this method the computational performance the common weight multi-criteria decision-making (MCDM) DEA model proposed by Karsak and Ahiska (International Journal of Production Research, 2005, 43(8), 1537-1554) is improved. This improvement is significant when computational issues and complexity analysis are a concern.

Traffic based energy consumption optimisation to improve the lifetime and performance of ad hoc wireless sensor networks

Ad hoc wireless sensor networks (WSNs) are formed from self-organising configurations of distributed, energy constrained, autonomous sensor nodes. The service lifetime of such sensor nodes depends on the power supply and the energy consumption, which is typically dominated by the communication subsystem. One of the key challenges in unlocking the potential of such data gathering sensor networks is conserving energy so as to maximize their post deployment active lifetime. This thesis described the research carried on the continual development of the novel energy efficient Optimised grids algorithm that increases the WSNs lifetime and improves on the QoS parameters yielding higher throughput, lower latency and jitter for next generation of WSNs. Based on the range and traffic relationship the novel Optimised grids algorithm provides a robust traffic dependent energy efficient grid size that minimises the cluster head energy consumption in each grid and balances the energy use throughout the network. Efficient spatial reusability allows the novel Optimised grids algorithm improves on network QoS parameters. The most important advantage of this model is that it can be applied to all one and two dimensional traffic scenarios where the traffic load may fluctuate due to sensor activities. During traffic fluctuations the novel Optimised grids algorithm can be used to re-optimise the wireless sensor network to bring further benefits in energy reduction and improvement in QoS parameters. As the idle energy becomes dominant at lower traffic loads, the new Sleep Optimised grids model incorporates the sleep energy and idle energy duty cycles that can be implemented to achieve further network lifetime gains in all wireless sensor network models. Another key advantage of the novel Optimised grids algorithm is that it can be implemented with existing energy saving protocols like GAF, LEACH, SMAC and TMAC to further enhance the network lifetimes and improve on QoS parameters. The novel Optimised grids algorithm does not interfere with these protocols, but creates an overlay to optimise the grids sizes and hence transmission range of wireless sensor nodes.

Comparing different approach and avoidance models of learning and personality in the prediction of work, university, and leadership outcomes

Jackson (2005) developed a hybrid model of personality and learning, known as the learning styles profiler (LSP) which was designed to span biological, socio-cognitive, and experiential research foci of personality and learning research. The hybrid model argues that functional and dysfunctional learning outcomes can be best understood in terms of how cognitions and experiences control, discipline, and re-express the biologically based scale of sensation-seeking. In two studies with part-time workers undertaking tertiary education (N=137 and 58), established models of approach and avoidance from each of the three different research foci were compared with Jackson's hybrid model in their predictiveness of leadership, work, and university outcomes using self-report and supervisor ratings. Results showed that the hybrid model was generally optimal and, as hypothesized, that goal orientation was a mediator of sensation-seeking on outcomes (work performance, university performance, leader behaviours, and counterproductive work behaviour). Our studies suggest that the hybrid model has considerable promise as a predictor of work and educational outcomes as well as dysfunctional outcomes.

The impact of performance measurement in strategic planning

Purpose – To investigate the impact of performance measurement in strategic planning process. Design/methodology/approach – A large scale survey was conducted online with Warwick Business School alumni. The questionnaire was based on the Strategic Development Process model by Dyson. The questionnaire was designed to map the current practice of strategic planning and to determine its most influential factors on the effectiveness of the process. All questions were close ended and a seven-point Likert scale used. The independent variables were grouped into four meaningful factors by factor analysis (Varimax, coefficient of rotation 0.4). The factors produced were used to build regression models (stepwise) for the five assessments of strategic planning process. Regression models were developed for the totality of the responses, comparing SMEs and large organizations and comparing organizations operating in slowly and rapidly changing environments. Findings – The results indicate that performance measurement stands as one of the four main factors characterising the current practice of strategic planning. This research has determined that complexity coming from organizational size and rate of change in the sector creates variation in the impact of performance measurement in strategic planning. Large organizations and organizations operating in rapidly changing environments make greater use of performance measurement. Research limitations/implications – This research is based on subjective data, therefore the conclusions do not concern the impact of strategic planning process' elements on the organizational performance achievements, but on the success/effectiveness of the strategic planning process itself. Practical implications – This research raises a series of questions about the use and potential impact of performance measurement, especially in the categories of organizations that are not significantly influenced by its utilisation. It contributes to the field of performance measurement impact. Originality/value – This research fills in the gap literature concerning the lack of large scale surveys on strategic development processes and performance measurement. It also contributes in the literature of this field by providing empirical evidences on the impact of performance measurement upon the strategic planning process.

Maintenance strategies affecting equipment performance

Proper maintenance of plant items is crucial for the safe and profitable operation of process plants, The relevant maintenance policies fall into the following four categories: (i) preventivejopportunistic/breakdown replacement policies, (ii) inspection/inspection-repair-replacernent policies, (iii) restorative maintenance policies, and (iv) condition based maintenance policies, For correlating failure times of component equipnent and complete systems, the Weibull failure distribution has been used, A new powerful method, SEQLIM, has been proposed for the estimation of the Weibull parameters; particularly, when maintenance records contain very few failures and many successful operation times. When a system consists of a number of replaceable, ageing components, an opporturistic replacernent policy has been found to be cost-effective, A simple opportunistic rrodel has been developed. Inspection models with various objective functions have been investigated, It was found that, on the assumption of a negative exponential failure distribution, all models converge to the same optimal inspection interval; provided the safety components are very reliable and the demand rate is low, When deterioration becomes a contributory factor to same failures, periodic inspections, calculated from above models, are too frequent, A case of safety trip systems has been studied, A highly effective restorative maintenance policy can be developed if the performance of the equipment under this category can be related to some predictive modelling. A novel fouling model has been proposed to determine cleaning strategies of condensers, Condition-based maintenance policies have been investigated. A simple gauge has been designed for condition monitoring of relief valve springs. A typical case of an exothermic inert gas generation plant has been studied, to demonstrate how various policies can be applied to devise overall maintenance actions.

Bayesian online algorithms for learning in discrete Hidden Markov Models

We propose and analyze two different Bayesian online algorithms for learning in discrete Hidden Markov Models and compare their performance with the already known Baldi-Chauvin Algorithm. Using the Kullback-Leibler divergence as a measure of generalization we draw learning curves in simplified situations for these algorithms and compare their performances.

Techniques to improve forecasting models: applications to energy demand and price

This thesis is a study of three techniques to improve performance of some standard fore-casting models, application to the energy demand and prices. We focus on forecasting demand and price one-day ahead. First, the wavelet transform was used as a pre-processing procedure with two approaches: multicomponent-forecasts and direct-forecasts. We have empirically compared these approaches and found that the former consistently outperformed the latter. Second, adaptive models were introduced to continuously update model parameters in the testing period by combining ?lters with standard forecasting methods. Among these adaptive models, the adaptive LR-GARCH model was proposed for the fi?rst time in the thesis. Third, with regard to noise distributions of the dependent variables in the forecasting models, we used either Gaussian or Student-t distributions. This thesis proposed a novel algorithm to infer parameters of Student-t noise models. The method is an extension of earlier work for models that are linear in parameters to the non-linear multilayer perceptron. Therefore, the proposed method broadens the range of models that can use a Student-t noise distribution. Because these techniques cannot stand alone, they must be combined with prediction models to improve their performance. We combined these techniques with some standard forecasting models: multilayer perceptron, radial basis functions, linear regression, and linear regression with GARCH. These techniques and forecasting models were applied to two datasets from the UK energy markets: daily electricity demand (which is stationary) and gas forward prices (non-stationary). The results showed that these techniques provided good improvement to prediction performance.

Flow patterns and performance of distillation trays

It is known that distillation tray efficiency depends on the liquid flow pattern, particularly for large diameter trays. Scale·up failures due to liquid channelling have occurred, and it is known that fitting flow control devices to trays sometirr.es improves tray efficiency. Several theoretical models which explain these observations have been published. Further progress in understanding is at present blocked by lack of experimental measurements of the pattern of liquid concentration over the tray. Flow pattern effects are expected to be significant only on commercial size trays of a large diameter and the lack of data is a result of the costs, risks and difficulty of making these measurements on full scale production columns. This work presents a new experiment which simulates distillation by water cooling. and provides a means of testing commercial size trays in the laboratory. Hot water is fed on to the tray and cooled by air forced through the perforations. The analogy between heat and mass transfer shows that the water temperature at any point is analogous to liquid concentration and the enthalpy of the air is analogous to vapour concentration. The effect of the liquid flow pattern on mass transfer is revealed by the temperature field on the tray. The experiment was implemented and evaluated in a column of 1.2 m. dia. The water temperatures were measured by thennocouples interfaced to an electronic computerised data logging system. The "best surface" through the experimental temperature measurements was obtained by the mathematical technique of B. splines, and presented in tenos of lines of constant temperature. The results revealed that in general liquid channelling is more imponant in the bubbly "mixed" regime than in the spray regime. However, it was observed that severe channelling also occurred for intense spray at incipient flood conditions. This is an unexpected result. A computer program was written to calculate point efficiency as well as tray efficiency, and the results were compared with distillation efficiencies for similar loadings. The theoretical model of Porter and Lockett for predicting distillation was modified to predict water cooling and the theoretical predictions were shown to be similar to the experimental temperature profiles. A comparison of the repeatability of the experiments with an errors analysis revealed that accurate tray efficiency measurements require temperature measurements to better than ± 0.1 °c which is achievable with conventional techniques. This was not achieved in this work, and resulted in considerable scatter in the efficiency results. Nevertheless it is concluded that the new experiment is a valuable tool for investigating the effect of the liquid flow pattern on tray mass transfer.

Forecasting exchange rates with linear and nonlinear models

In this paper the exchange rate forecasting performance of neural network models are evaluated against random walk and a range of time series models. There are no guidelines available that can be used to choose the parameters of neural network models and therefore the parameters are chosen according to what the researcher considers to be the best. Such an approach, however, implies that the risk of making bad decisions is extremely high which could explain why in many studies neural network models do not consistently perform better than their time series counterparts. In this paper through extensive experimentation the level of subjectivity in building neural network models is considerably reduced and therefore giving them a better chance of performing well. Our results show that in general neural network models perform better than traditionally used time series models in forecasting exchange rates.

Ethnic diversity as a multilevel construct:the combined effects of dissimilarity, group diversity, and societal status on learning performance in work groups

The authors present a model of the multilevel effects of diversity on individual learning performance in work groups. For ethnically diverse work groups, the model predicts that group diversity elicits either positive or negative effects on individual learning performance, depending on whether a focal individual’s ethnic dissimilarity from other group members is high or low. By further considering the societal status of an individual’s ethnic origin within society (Anglo versus non-Anglo for our U.K. context), the authors hypothesize that the model’s predictions hold more strongly for non-Anglo group members than for Anglo group members. We test this model with data from 412 individuals working on a 24-week business simulation in 87 four- to seven-person groups with varying degrees of ethnic diversity. Two of the three hypotheses derived from the model received full support and one hypothesis received partial support. Implications for theory development, methods, and practice in applied group diversity research are discussed.

Measuring the operational performance of intensive care units using the analytic hierarchy process approach

Purpose - The purpose of this study is to develop a performance measurement model for service operations using the analytic hierarchy process approach. Design/methodology/approach - The study reviews current relevant literature on performance measurement and develops a model for performance measurement. The model is then applied to the intensive care units (ICUs) of three different hospitals in developing nations. Six focus group discussions were undertaken, involving experts from the specific area under investigation, in order to develop an understandable performance measurement model that was both quantitative and hierarchical. Findings - A combination of outcome, structure and process-based factors were used as a foundation for the model. The analyses of the links between them were used to reveal the relative importance of each and their associated sub factors. It was considered to be an effective quantitative tool by the stakeholders. Research limitations/implications - This research only applies the model to ICUs in healthcare services. Practical implications - Performance measurement is an important area within the operations management field. Although numerous models are routinely being deployed both in practice and research, there is always room for improvement. The present study proposes a hierarchical quantitative approach, which considers both subjective and objective performance criteria. Originality/value - This paper develops a hierarchical quantitative model for service performance measurement. It considers success factors with respect to outcomes, structure and processes with the involvement of the concerned stakeholders based upon the analytic hierarchy process approach. The unique model is applied to the ICUs of hospitals in order to demonstrate its effectiveness. The unique application provides a comparative international study of service performance measurement in ICUs of hospitals in three different countries. © Emerald Group Publishing Limited.

Assessing pupil and school performance by non-parametric and parametric techniques

Often observations are nested within other units. This is particularly the case in the educational sector where school performance in terms of value added is the result of school contribution as well as pupil academic ability and other features relating to the pupil. Traditionally, the literature uses parametric (i.e. it assumes a priori a particular function on the production process) Multi-Level Models to estimate the performance of nested entities. This paper discusses the use of the non-parametric (i.e. without a priori assumptions on the production process) Free Disposal Hull model as an alternative approach. While taking into account contextual characteristics as well as atypical observations, we show how to decompose non-parametrically the overall inefficiency of a pupil into a unit specific and a higher level (i.e. a school) component. By a sample of entry and exit attainments of 3017 girls in British ordinary single sex schools, we test the robustness of the non-parametric and parametric estimates. We find that the two methods agree in the relative measures of the scope for potential attainment improvement. Further, the two methods agree on the variation in pupil attainment and the proportion attributable to pupil and school level.

A study of the effect of harmonics in the system on the performance of residual current-operated circuit-breakers and electromechanical overcurrent relays

Residual current-operated circuit-breakers (RCCBs) have proved useful devices for the protection of both human beings against ventricular fibrillation and installations against fire. Although they work well with sinusoidal waveforms, there is little published information on their characteristics. Due to shunt connected non-linear devices, not the least of which is the use of power electronic equipment, the supply is distorted. Consequently, RCCBs as well as other protection relays are subject to non-sinusoidal current waveforms. Recent studies showed that RCCBs are greatly affected by harmonics, however the reasons for this are not clear. A literature search has also shown that there are inconsistencies in the analysis of the effect of harmonics on protection relays. In this work, the way RCCBs operate is examined, then a model is built with the aim of assessing the effect of non-sinusoidal current on RCCBs. Tests are then carried out on a number of RCCBs and these, when compared with the results from the model showed good correlation. In addition, the model also enables us to explain the RCCBs characteristics for pure sinusoidal current. In the model developed, various parameters are evaluated but special attention is paid to the instantaneous value of the current and the tripping mechanism movement. A similar assessment method is then used to assess the effect of harmonics on two types of protection relay, the electromechanical instantaneous relay and time overcurrent relay. A model is built for each of them which is then simulated on the computer. Tests results compare well with the simulation results, and thus the model developed can be used to explain the relays behaviour in a harmonics environment. The author's models, analysis and tests show that RCCBs and protection relays are affected by harmonics in a way determined by the waveform and the relay constants. The method developed provides a useful tool and the basic methodology to analyse the behaviour of RCCBs and protection relays in a harmonics environment. These results have many implications, especially the way RCCBs and relays should be tested if harmonics are taken into account.