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Eskom is the major electricity supplier in South Africa and medium term forecasting within the company is a critical activity to ensure that enough electricity is generated to support the country's growth, that the networks can supply the electricity and that the revenue derived from electricity consumption is managed efficiently. This study investigates the most suitable forecasting technique for predicting monthly electricity consumption, one year ahead for four major municipalities within Kwa-Zulu Natal. / Thesis (M.Sc.)-University of Natal, Pietermaritzburg, 1997.

Access to abstract permanently restricted to Ball State community only. / Estimation and forecasting of time series data -- Fitting of Saudi stock price by deterministic models -- Determination and fitting of the ARIMA models for Saudi stock price data -- Evaluation of forecasts by cross validation. / Access to thesis permanently restricted to Ball State community only. / Department of Mathematical Sciences

In this work, we analyze the data from 1.5 m telescope of Tubitak National Observatory (TUG) for two objects V2275 Cyg and RW UMi. After applying aperture and PSF photometry to the CCD data, we derive the light curve of the objects. Also we apply discrete Fourier transform and Scargle time series analysis to obtain power spectrum and search for periodicities in their power spectra in which both systems show interesting properties. We discover large variations in V2275 Cyg due to irradiation effects on the secondary, with an orbital period Porb = 0.316&plusmn / 0.007 day. We also detect a very short orbital period for RW UMi Porb = 1.96&plusmn / 0.073h and also a spin period Pspin = 1.29&plusmn / 0.036h in the presence of several beat frequencies. These results (if con&amp / #64257 / rmed) lead us to say that RW UMi can be an intermediate of polar system.

This research addresses the problem of temporal pattern discovery in real-valued, multivariate sensor data. Several algorithms were developed, and subsequent evaluation demonstrates that they can efficiently and accurately discover unknown recurring patterns in time series data taken from many different domains. Different data representations and motif models were investigated in order to design an algorithm with an improved balance between run-time and detection accuracy. The different data representations are used to quickly filter large data sets in order to detect potential patterns that form the basis of a more detailed analysis. The representations include global discretization, which can be efficiently analyzed using a suffix tree, local discretization with a corresponding random projection algorithm for locating similar pairs of subsequences, and a density-based detection method that operates on the original, real-valued data. In addition, a new variation of the multivariate motif discovery problem is proposed in which each pattern may span only a subset of the input features. An algorithm that can efficiently discover such "subdimensional" patterns was developed and evaluated. The discovery algorithms are evaluated by measuring the detection accuracy of discovered patterns relative to a set of expected patterns for each data set. The data sets used for evaluation are drawn from a variety of domains including speech, on-body inertial sensors, music, American Sign Language video, and GPS tracks.

This thesis describes a new approach to achieving high quality distributed garbage collection using the Train Algorithm. This algorithm has been investigated for its ability to provide high quality collection in a variety of contexts, including persistent object systems and distributed object systems. Prior literature on the distributed Train Algorithm suggests that safe, complete, asynchronous, and scalable collection can be attained, however an approach that achieves this combination of behaviour has yet to emerge. The mechanisms and policies described in this thesis are unique in their ability to exploit the distributed Train Algorithm in a manner that displays all four desirable qualities. Further the mechanisms allow any number of mutator and collector threads to operate concurrently within a site; this is also a unique property amongst train-based mechanisms (distributed or otherwise). Confidence in the quality of the approach promoted in this thesis is obtained via a top-down approach. Firstly a concise behavioural model is introduced to capture fundamental requirements for safe and complete behaviour from train-based collection mechanisms. The model abstracts over the techniques previously introduced under the banner of the Train Algorithm. It serves as a self- contained template for correct train-based collection that is independent of a target object system for deployment of the algorithm. Secondly a means to instantiate the model in a distributed object system is described. The instantiation includes well-established techniques from prior literature, and via the model these are correctly refined and reorganised with new techniques to achieve asynchrony, scalability, and support for concurrency. The result is a flexible approach that allows a distributed system to exhibit a variety of local collection mechanisms and policies, while ensuring their interaction is safe, complete, asynchronous, and scalable regardless of the local choices made by each site. Additional confidence in the properties of the new approach is obtained from implementation within a distributed object system simulation. The implementation provides some insight into the practical issues that arise through the combination of distribution, concurrent execution within sites, and train-based collection. Executions of the simulation system are used to verify that safe collection is observed at all times, and obtain evidence that asynchrony, scalability, and concurrency can be observed in practice. / Thesis (Ph.D.)--School of Computer Science, 2004.

This thesis presents models that describe the behaviour of electricity demand and ambient temperature. Important features of both variables are described by mathematical components. These models were developed to calculate the value of electricity demand that is not expected to be exceeded more than once in ten years and to generate synthetic sequences that can be used as input data in simulation software. / PhD Doctorate

Thesis (Ph.D.)--Mississippi State University. Department of Mathematics and Statistics. / Title from title screen. Includes bibliographical references.

Thesis (M.E.-Res.)--University of Wollongong, 2008. / Typescript. Includes bibliographical references: p. 164-179.

Thesis (M.S.)--State University of New York at Binghamton, Thomas J. Watson School of Engineering and Applied Science, Department of Computer Science, 2009. / Includes bibliographical references.