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.

Exchange rate forecasting using a combined parametric and nonparametric self-organising modelling approach

Financial prediction has attracted a lot of interest due to the financial implications that the accurate prediction of financial markets can have. A variety of data driven modellingapproaches have been applied but their performance has produced mixed results. In this study we apply both parametric (neural networks with active neurons) and nonparametric (analog complexing) self-organisingmodelling methods for the daily prediction of the exchangerate market. We also propose acombinedapproach where the parametric and nonparametricself-organising methods are combined sequentially, exploiting the advantages of the individual methods with the aim of improving their performance. The combined method is found to produce promising results and to outperform the individual methods when tested with two exchangerates: the American Dollar and the Deutche Mark against the British Pound.

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.

A Bayesian state space modelling approach to probabilistic quantitative precipitation forecasting

The generation of very short range forecasts of precipitation in the 0-6 h time window is traditionally referred to as nowcasting. Most existing nowcasting systems essentially extrapolate radar observations in some manner, however, very few systems account for the uncertainties involved. Thus deterministic forecast are produced, which have a limited use when decisions must be made, since they have no measure of confidence or spread of the forecast. This paper develops a Bayesian state space modelling framework for quantitative precipitation nowcasting which is probabilistic from conception. The model treats the observations (radar) as noisy realisations of the underlying true precipitation process, recognising that this process can never be completely known, and thus must be represented probabilistically. In the model presented here the dynamics of the precipitation are dominated by advection, so this is a probabilistic extrapolation forecast. The model is designed in such a way as to minimise the computational burden, while maintaining a full, joint representation of the probability density function of the precipitation process. The update and evolution equations avoid the need to sample, thus only one model needs be run as opposed to the more traditional ensemble route. It is shown that the model works well on both simulated and real data, but that further work is required before the model can be used operationally. © 2004 Elsevier B.V. All rights reserved.

Sales manager problem resolution styles:measure development and an examination of their salesperson-related consequences

Personal selling and sales management play a critical role in the short and long term success of the firm, and have thus received substantial academic interest since the 1970s. Sales research has examined the role of the sales manager in some depth, defining a number of key technical and interpersonal roles which sales managers have in influencing sales force effectiveness. However, one aspect of sales management which appears to remain unexplored is that of their resolution of salesperson-related problems. This study represents the first attempt to address this gap by reporting on the conceptual and empirical development of an instrument designed to measure sales managers' problem resolution styles. A comprehensive literature review and qualitative research study identified three key constructs relating to sales managers' problem resolution styles. The three constructs identified were termed; sales manager willingness to respond, sales manager caring, and sales manager aggressiveness. Building on this, existing literature was used to develop a conceptual model of salesperson-specific consequences of the three problem resolution style constructs. The quantitative phase of the study consisted of a mail survey of UK salespeople, achieving a total sample of 140 fully usable responses. Rigorous statistical assessment of the sales manager problem resolution style measures was undertaken, and construct validity examined. Following this, the conceptual model was tested using latent variable path analysis. The results for the model were encouraging overall, and also with regard to the individual hypotheses. Sales manager problem resolution styles were found individually to have significant impacts on the salesperson-specific variables of role ambiguity, emotional exhaustion, job satisfaction, organisational commitment and organisational citizenship behaviours. The findings, theoretical and managerial implications, limitations and directions for future research are discussed.

Determinants of sales performance in insurance industry:a cross-cultural comparison between the UK and Malaysia

This thesis is concerned with an empirical investigation of the factors that predict a successful salesperson, using a cross-cultural comparison of two countries: the UK and Malaysia. Besides collecting quantitative data, qualitative data on organisational, environmental and cultural factors were also collected through interviews, personal and case observations. The quantitative data consist of sixteen independent factors and three dependent factors. The independent variables include self-efficacy, self-esteem, locus of control, self-monitoring, extrinsic motivation, intrinsic motivation, experience, training perception, role ambiguity, role conflict, role inaccuracy, gender, age, education, race and religion. The dependent variables are performance target achieved, performance earnings and performance ratings. Questionnaires were distributed to about 500 salespersons in each country, from three insurance companies in the UK and two insurance companies in Malaysia. Response rates were 75 and 50 percent from the UK and Malaysia respectively. The survey results indicated that a salesperson's performance in the UK is predicted by self-efficacy, internal locus of control, self-esteem, extrinsic motivation, experience, training perceptions, role conflict and gender. In Malaysia, a salesperson's performance is predicted by self-efficacy, self-monitoring, experience, role conflict, role ambiguity, education, gender, race and religion. Self-efficacy, experience, role conflict and gender are common predictors of salespersons' performance in both cultures. The likely explanation for these results is culture differences, i.e. UK has a homogeneous culture, while Malaysia has a heterogeneous one. Results from the case observations, such as organisational and environmental factors, give supporting evidence in explaining the empirical results. Implications from the findings are discussed from two aspects: (1) theoretical implications for divergence/convergence theory, Hofstede's model, Churchill's model, and (2) managerial implications for selection, training, motivation and appraisal.

Forecasting and risk analysis applied to management planning and control

The aim of this research was to improve the quantitative support to project planning and control principally through the use of more accurate forecasting for which new techniques were developed. This study arose from the observation that in most cases construction project forecasts were based on a methodology (c.1980) which relied on the DHSS cumulative cubic cost model and network based risk analysis (PERT). The former of these, in particular, imposes severe limitations which this study overcomes. Three areas of study were identified, namely growth curve forecasting, risk analysis and the interface of these quantitative techniques with project management. These fields have been used as a basis for the research programme. In order to give a sound basis for the research, industrial support was sought. This resulted in both the acquisition of cost profiles for a large number of projects and the opportunity to validate practical implementation. The outcome of this research project was deemed successful both in theory and practice. The new forecasting theory was shown to give major reductions in projection errors. The integration of the new predictive and risk analysis technologies with management principles, allowed the development of a viable software management aid which fills an acknowledged gap in current technology.

A study in Britain and France of the development of cross-cultural in-house sales training using distance learning.

The thesis is concerned with cross-cultural distance learning in two countries: Great Britain and France. Taking the example of in-house sales training, it argues that it is possible to develop courses for use in two or more countries of differing culture and language. Two courses were developed by the researcher. Both were essentially print-based distance-learning courses designed to help salespeople achieve a better understanding of their customers. One used a quantitative, the other qualitative approach. One considered the concept of the return on investment and the other, for which a video support was also developed, considered the analysis of a customer's needs. Part 1 of the thesis considers differences in the training context between France and Britain followed by a review of the learning process with reference to distance learning. Part 2 looks at the choice of training medium course design and evaluation and sets out the methodology adopted, including problems encountered in this type of fieldwork. Part 3 analyses the data and draws conclusions from the findings, before offering a series of guidelines for those concerned with the development of cross-cultural in-house training courses. The results of the field tests on the two courses were analysed in relation to the socio-cultural, educational and experiential background of the learners as well as their preferred learning styles. The thesis argues that it is possible to develop effective in-house sales training courses to be used in two cultures and identifies key considerations which need to be taken into account when carrying out this type of work.

Schematic structure and the modulation of propositions in economics forecasting text

Working within the framework of the branch of Linguistics known as discourse analysis, and more specifically within the current approach of genre analysis, this thesis presents an analysis of the English of economic forecasting. The language of economic forecasting is highly specialised and follows certain conventions of structure and style. This research project identifies these characteristics and explains them in terms of their communicative function. The work is based on a corpus of texts published in economic reports and surveys by major corporate bodies. These documents are targeted at an international expert readership familiar with this genre. The data is analysed at two broad levels: firstly, the macro-level of text structure which is described in terms of schema-theory, a currently influential model of analysis, and, secondly, the micro-level of authors' strategies for modulating the predictions which form the key move in the forecasting schema. The thesis aims to contribute to the newly developing field of genre analysis in a number of ways: firstly, by a coverage of a hitherto neglected but intrinsically interesting and important genre (Economic Forecasting); secondly, by testing the applicability of existing models of analysis at the level of schematic structure and proposing a genre-specific model; thirdly by offering insights into the nature of modulation of propositions which is often broadly classified as `hedging' or `modality', and which has been recently described as lq`an area for prolonged fieldwork'. This phenomenon is shown to be a key feature of this particular genre. It is suggested that this thesis, in addition to its contribution to the theory of genre analysis, provides a useful basis for work by teachers of English for Economics, an important area of English for Specific Purposes.

Systems development of data retrieval for an engineering company's after-sales service

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Does money matter in inflation forecasting

This paper provides the most fully comprehensive evidence to date on whether or not monetary aggregates are valuable for forecasting US inflation in the early to mid 2000s. We explore a wide range of different definitions of money, including different methods of aggregation and different collections of included monetary assets. In our forecasting experiment we use two non-linear techniques, namely, recurrent neural networks and kernel recursive least squares regression - techniques that are new to macroeconomics. Recurrent neural networks operate with potentially unbounded input memory, while the kernel regression technique is a finite memory predictor. The two methodologies compete to find the best fitting US inflation forecasting models and are then compared to forecasts from a naive random walk model. The best models were non-linear autoregressive models based on kernel methods. Our findings do not provide much support for the usefulness of monetary aggregates in forecasting inflation.

Revolution in sales:The impact of social media and related technology on the selling environment

Over the years several articles have tracked the impact of technology on various aspects of the sales domain. However, the advent of social media and technologies related to social media has gone largely unnoticed in the literature. This article first provides brief attention to changing aspects of technology within the sales environment, leading to the identification of social media as a dominant new selling tool. A qualitative approach (focus groups) is employed to explore the breadth of current technology usage by sales managers and salespeople. Analysis of the data, collected in the United States and the United Kingdom, reveals six major themes: connectivity, relationships, selling tools, generational, global, and sales/marketing interface. Results provide evidence of a revolution in the buyer-seller relationship that includes some unanticipated consequences both for sales organization performance and needed future research contributions.