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.

Combining spatially distributed predictions from neural networks

In this report we discuss the problem of combining spatially-distributed predictions from neural networks. An example of this problem is the prediction of a wind vector-field from remote-sensing data by combining bottom-up predictions (wind vector predictions on a pixel-by-pixel basis) with prior knowledge about wind-field configurations. This task can be achieved using the scaled-likelihood method, which has been used by Morgan and Bourlard (1995) and Smyth (1994), in the context of Hidden Markov modelling

Towards a framework for combining stochastic and deterministic descriptions of nonstationary financial time series

We present in this paper ideas to tackle the problem of analysing and forecasting nonstationary time series within the financial domain. Accepting the stochastic nature of the underlying data generator we assume that the evolution of the generator's parameters is restricted on a deterministic manifold. Therefore we propose methods for determining the characteristics of the time-localised distribution. Starting with the assumption of a static normal distribution we refine this hypothesis according to the empirical results obtained with the methods anc conclude with the indication of a dynamic non-Gaussian behaviour with varying dependency for the time series under consideration.

On the annealed VC entropy for margin classifiers: A statistical mechanics study

Using techniques from Statistical Physics, the annealed VC entropy for hyperplanes in high dimensional spaces is calculated as a function of the margin for a spherical Gaussian distribution of inputs.

Advances in large margin classifiers

We apply methods of Statistical Mechanics to study the generalization performance of Support vector Machines in large data spaces.

Combining analytic hierarchy process and goal programming for logistics distribution network design

Logistics distribution network design is one of the major decision problems arising in contemporary supply chain management. The decision involves many quantitative and qualitative factors that may be conflicting in nature. This paper applies an integrated multiple criteria decision making approach to design an optimal distribution network. In the approach, the analytic hierarchy process (AHP) is used first to determine the relative importance weightings or priorities of alternative warehouses with respect to both deliverer oriented and customer oriented criteria. Then, the goal programming (GP) model incorporating the constraints of system, resource, and AHP priority is formulated to select the best set of warehouses without exceeding the limited available resources. In this paper, two commercial packages are used: Expert Choice for determining the AHP priorities of the warehouses, and LINDO for solving the GP model. © 2007 IEEE.

Combining data driven programming with component based software development:with applications in geovisualisation and dynamic data driven application systems

Software development methodologies are becoming increasingly abstract, progressing from low level assembly and implementation languages such as C and Ada, to component based approaches that can be used to assemble applications using technologies such as JavaBeans and the .NET framework. Meanwhile, model driven approaches emphasise the role of higher level models and notations, and embody a process of automatically deriving lower level representations and concrete software implementations. The relationship between data and software is also evolving. Modern data formats are becoming increasingly standardised, open and empowered in order to support a growing need to share data in both academia and industry. Many contemporary data formats, most notably those based on XML, are self-describing, able to specify valid data structure and content, and can also describe data manipulations and transformations. Furthermore, while applications of the past have made extensive use of data, the runtime behaviour of future applications may be driven by data, as demonstrated by the field of dynamic data driven application systems. The combination of empowered data formats and high level software development methodologies forms the basis of modern game development technologies, which drive software capabilities and runtime behaviour using empowered data formats describing game content. While low level libraries provide optimised runtime execution, content data is used to drive a wide variety of interactive and immersive experiences. This thesis describes the Fluid project, which combines component based software development and game development technologies in order to define novel component technologies for the description of data driven component based applications. The thesis makes explicit contributions to the fields of component based software development and visualisation of spatiotemporal scenes, and also describes potential implications for game development technologies. The thesis also proposes a number of developments in dynamic data driven application systems in order to further empower the role of data in this field.

Hierarchical classification of G-protein-coupled receptors with data-driven selection of attributes and classifiers

We address the important bioinformatics problem of predicting protein function from a protein's primary sequence. We consider the functional classification of G-Protein-Coupled Receptors (GPCRs), whose functions are specified in a class hierarchy. We tackle this task using a novel top-down hierarchical classification system where, for each node in the class hierarchy, the predictor attributes to be used in that node and the classifier to be applied to the selected attributes are chosen in a data-driven manner. Compared with a previous hierarchical classification system selecting classifiers only, our new system significantly reduced processing time without significantly sacrificing predictive accuracy.

Combining temporal and spectral information with spatial mapping to identify differences between phonological and semantic networks:a magnetoencephalographic approach

Early, lesion-based models of language processing suggested that semantic and phonological processes are associated with distinct temporal and parietal regions respectively, with frontal areas more indirectly involved. Contemporary spatial brain mapping techniques have not supported such clear-cut segregation, with strong evidence of activation in left temporal areas by both processes and disputed evidence of involvement of frontal areas in both processes. We suggest that combining spatial information with temporal and spectral data may allow a closer scrutiny of the differential involvement of closely overlapping cortical areas in language processing. Using beamforming techniques to analyze magnetoencephalography data, we localized the neuronal substrates underlying primed responses to nouns requiring either phonological or semantic processing, and examined the associated measures of time and frequency in those areas where activation was common to both tasks. Power changes in the beta (14-30 Hz) and gamma (30-50 Hz) frequency bandswere analyzed in pre-selected time windows of 350-550 and 500-700ms In left temporal regions, both tasks elicited power changes in the same time window (350-550 ms), but with different spectral characteristics, low beta (14-20 Hz) for the phonological task and high beta (20-30 Hz) for the semantic task. In frontal areas (BA10), both tasks elicited power changes in the gamma band (30-50 Hz), but in different time windows, 500-700ms for the phonological task and 350-550ms for the semantic task. In the left inferior parietal area (BA40), both tasks elicited changes in the 20-30 Hz beta frequency band but in different time windows, 350-550ms for the phonological task and 500-700ms for the semantic task. Our findings suggest that, where spatial measures may indicate overlapping areas of involvement, additional beamforming techniques can demonstrate differential activation in time and frequency domains. © 2012 McNab, Hillebrand, Swithenby and Rippon.

Combining ERP systems with enterprise 2.0

The paper discusses both the complementary factors and contradictions of adoption ERP based systems with enterprise 2.0. ERP is well known as its' efficient business process management. Also the high failure rate the system implementation is famous as well. According to [1], ERP systems could achieve efficient business performance by enabling a standardized business process design, but at a cost of flexibility in operations. However, enterprise 2.0 supports flexible business process management, informal and less structured interactions [3],[4],[21]. Traditional researcher claimed efficiency and flexibility may seem incompatible in that they are different business objectives and may exist in different organizational environments. However, the paper will break traditional norms that combine ERP and enterprise 2.0 in a single enterprise to improve both efficient and flexible operations simultaneously. Based on the multiple cases studies, four cases presented different attitudes on usage ERP systems and enterprise social systems. Based on socio-technical theory, the paper presents in-depth analysis benefits of combination ERP with enterprise 2.0 for these firms.

Ontology augmentation:combining semantic web and text resources

This work investigates the process of selecting, extracting and reorganizing content from Semantic Web information sources, to produce an ontology meeting the specifications of a particular domain and/or task. The process is combined with traditional text-based ontology learning methods to achieve tolerance to knowledge incompleteness. The paper describes the approach and presents experiments in which an ontology was built for a diet evaluation task. Although the example presented concerns the specific case of building a nutritional ontology, the methods employed are domain independent and transferrable to other use cases. © 2011 ACM.

How weak categorizers based upon different principles strengthen performance

Combining the results of classifiers has shown much promise in machine learning generally. However, published work on combining text categorizers suggests that, for this particular application, improvements in performance are hard to attain. Explorative research using a simple voting system is presented and discussed in the light of a probabilistic model that was originally developed for safety critical software. It was found that typical categorization approaches produce predictions which are too similar for combining them to be effective since they tend to fail on the same records. Further experiments using two less orthodox categorizers are also presented which suggest that combining text categorizers can be successful, provided the essential element of ‘difference’ is considered.

Organisational receptivity for change:combining context and capability to explain competitive advantage

This thesis explores efforts to conjoin organisational contexts and capabilities in explaining sustainable competitive advantage. Oliver (1997) argued organisations need to balance the need to conform to industry’s requirements to attain legitimization (e.g. DiMaggio & Powell, 1983), and the need for resource optimization (e.g. Barney, 1991). The author hypothesized that such balance can be viewed as movements along the homogeneity-heterogeneity continuum. An organisation in a homogenous industry possesses similar characteristics as its competitors, as opposed to a heterogeneous industry in which organisations within are differentiated and competitively positioned (Oliver, 1997). The movement is influenced by the dynamic environmental conditions that an organisation is experiencing. The author extended Oliver’s (1997) propositions of combining RBV’s focus on capabilities with institutional theory’s focus on organisational context, as well as redefining organisational receptivity towards change (ORC) factors from Butler and Allen’s (2008) findings. The authors contributed to the theoretical development of ORC theory to explain the attainment of sustainable competitive advantage. ORC adopts the assumptions from both institutional and RBV theories, where the receptivity factors include both organisational contexts and capabilities. The thesis employed a mixed method approach in which sequential qualitative quantitative studies were deployed to establish a robust, reliable, and valid ORC scale. The adoption of Hinkin’s (1995) three-phase scale development process was updated, thus items generated from interviews and literature reviews went through numerous exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) to achieve convergent, discriminant, and nomological validities. Samples in the first phase (semi structured interviews) were hotel owners and managers. In the second phase, samples were MBA students, and employees of private and public sectors. In the third phase, samples were hotel managers. The final ORC scale is a parsimonious second higher-order latent construct. The first-order constructs comprises four latent receptivity factors which are ideological vision (4 items), leading change (4 items), implementation capacity (4 items), and change orientation (7 items). Hypotheses testing revealed that high levels of perceived environmental uncertainty leads to high levels of receptivity factor. Furthermore, the study found a strong positive correlation between receptivity factors and competitive advantage, and between receptivity factors and organisation performance. Mediation analyses revealed that receptivity factors partially mediate the relationship between perceived environmental uncertainty, competitive advantage and organisational performance.

Mean field methods for Gaussian process classifiers

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Combining deformable templates and neural networks for HCR

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