Combining the wavelet transform and forecasting models to predict gas forward prices

This paper presents a forecasting technique for forward energy prices, one day ahead. This technique combines a wavelet transform and forecasting models such as multi- layer perceptron, linear regression or GARCH. These techniques are applied to real data from the UK gas markets to evaluate their performance. The results show that the forecasting accuracy is improved significantly by using the wavelet transform. The methodology can be also applied to forecasting market clearing prices and electricity/gas loads.

Reasserting the distinctive character of share-holder remedies in Scotland - Wilson v Inverness Retail and Business Park Ltd

Juridical Review. Looks at the question of whether an individual shareholder has title to bring an action on the company's behalf in exceptional circumstances, as considered in the cases of Anderson v Hogg and Wilson v Inverness Retail & Business Park Ltd. Examines the difference between English and Scottish law in this area, notwithstanding the reliance on English case law in Scotland due to the small number of Scottish cases decided. Looks at progress towards the reform of company law and the impact it will have on a shareholder's title to sue.

Using process mapping and business process simulation to support a process-based approach to change in a public sector organisation

A case study demonstrates the use of a process-based approach to change regarding the implementation of an information system for road traffic accident reporting in a UK police force. The supporting tools of process mapping and business process simulation are used in the change process and assist in communicating the current process design and people's roles in the overall performance of that design. The simulation model is also used to predict the performance of new designs incorporating the use of information technology. The approach is seen to have a number of advantages in the context of a public sector organisation. These include the ability for personnel to move from a traditional grouping of staff in occupational groups with relationships defined by reporting requirements to a view of their role in a process, which delivers a performance to a customer. By running the simulation through time it is also possible to gauge how changes at an operational level can lead to the meeting of strategic targets over time. Also the ability of simulation to proof new designs was seen as particularly important in a government agency were past failures of information technology investments had contributed to a more risk averse approach to their implementation. © 2004 Elsevier Ltd. All rights reserved.

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.

Numerical Q parameter estimates for scalar and vector models in optical communication system simulations

The Q parameter scales differently with the noise power for the signal-noise and the noise-noise beating terms in scalar and vector models. Some procedures for including noise in the scalar model largely under-estimate the Q parameter. We propose a simple method for including noise within a scalar model which will allow both the noise-noise dominated limit and the signal-noise dominated limit to be treated consistently. © 2005 Elsevier B.V. All rights reserved.

Q parameter scaling in scalar and vector models with dominant signal-noise and noise-noise beating terms

The Q parameter scales differently with the noise power for the signal-noise and the noise-noise beating terms in scalar and vector models. Some procedures for including noise in the scalar model largely under-estimate the Q parameter.

Service innovation, embeddedness and business performance:evidence from Northern Ireland

This paper explores the factors that determine innovation by service firms, and in particular the contribution of intra- and extra-regional connectivity. Subsequently, it is examined how service firms' innovation activity relates to productivity and export behaviour. The empirical analysis is based on matched data from the 2005 UK Innovation Survey - the UK component of the 4th Community Innovation Survey (CIS) - and the Annual Business Inquiry for Northern Ireland. Evidence is found of negative intra-regional embeddedness effects, but there is a positive contribution to innovation from extra-regional connectivity, particularly links to customers. Relationships between innovation, exporting, and productivity prove complex, but suggest that innovation itself is not sufficient to generate productivity improvements. Only when innovation is combined with increased export activity are productivity gains evident.

Service innovation, embeddedness and business performance:UK regional evidence

This paper asks to question. First, what types of linkages make firms in the service sector innovate? And second, what is the link between innovation and the firms’ productivity and export performance? Using survey data from Northern Ireland we find that links intra-regional links (i.e. within Northern Ireland) to customers, suppliers and universities have little effect on innovation, but external links (i.e. outside Northern Ireland) help to boost innovation. Relationships between innovation, exporting and productivity prove complex but suggest that innovation itself is not sufficient to generate productivity improvements. Only when innovation is combined with increased export activity are productivity gains produced. This suggests that regional innovation policy should be oriented towards helping firms to innovate only where it helps firms to enter export markets or to expand their existing export market presence.

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.

Short-term electricity demand and gas price forecasts using wavelet transforms and adaptive models

This paper presents some forecasting techniques for energy demand and price prediction, one day ahead. These techniques combine wavelet transform (WT) with fixed and adaptive machine learning/time series models (multi-layer perceptron (MLP), radial basis functions, linear regression, or GARCH). To create an adaptive model, we use an extended Kalman filter or particle filter to update the parameters continuously on the test set. The adaptive GARCH model is a new contribution, broadening the applicability of GARCH methods. We empirically compared two approaches of combining the WT with prediction models: multicomponent forecasts and direct forecasts. These techniques are applied to large sets of real data (both stationary and non-stationary) from the UK energy markets, so as to provide comparative results that are statistically stronger than those previously reported. The results showed that the forecasting accuracy is significantly improved by using the WT and adaptive models. The best models on the electricity demand/gas price forecast are the adaptive MLP/GARCH with the multicomponent forecast; their MSEs are 0.02314 and 0.15384 respectively.