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Thesis (Ph. D.)--University of Hong Kong, 2007. / Title proper from title frame. Also available in printed format.

Thesis (Ph. D.)--University of Hong Kong, 2008.

Thesis (M.S.)--Wisconsin. / Biography: leaves 73-76.

Thesis (M. Phil.)--University of Hong Kong, 1993.

Thesis (Ph.D.)--University of Iowa, 2009. / Thesis supervisor: John Geweke. Includes bibliographical references (leaves 71-73).

Thesis (M.S. in Applied Mathematics) Naval Postgraduate School, June 1997. / Thesis advisors, Christopher Frenzen, Philip Beaver. Includes bibliographical references (p. 103-104). Also available online.

Electricity load forecasts now form an essential part of the routine operations of electricity companies. The complexity of the short-term load forecasting (STLF) problem arises from the multiple seasonal components, the change in consumer behaviour during holiday seasons and other social and religious events that affect electricity consumption. The aim of this research is to produce models for electricity demand that can be used to further the understanding of the dynamics of electricity consumption in South Wales. These models can also be used to produce weather corrected forecasts, and to provide short-term load forecasts. Two novel time series modelling approaches were introduced and developed. Profiles ARIMA (PARIMA) and the Variability Decomposition Method (VDM). PARIMA is a univariate modelling approach that is based on the hierarchical modelling of the different components of the electricity demand series as deterministic profiles, and modelling the remainder stochastic component as ARIMA, serving as a simple yet versatile signal extraction procedure and as a powerful prewhitening technique. The VDM is a robust transfer function modelling approach that is based on decomposing the variability in time series data to that of inherent and external. It focuses the transfer function model building on explaining the external variability of the data and produces models with parameters that are pertinent to the components of the series. Several candidate input variables for the VDM models for electricity demand were investigated, and a novel collective measure of temperature the Fair Temperature Value (FTV) was introduced. The FTV takes into account the changes in variance of the daily maximum and minimum temperatures with time, making it a more suitable explanatory variable for the VDM model. The novel PARIMA and VDM approaches were used to model the quarterly, monthly, weekly, and daily demand series. Both approaches succeeded where existing approaches were unsuccessful and, where comparisons are possible, produced models that were superior in performance. The VDM model with the FTV as its explanatory variable was the best performing model in the analysis and was used for weather correction. Here, weather corrected forecasts were produced using the weather sensitive components of the PARIMA models and the FTV transfer function component of the VDM model.

This thesis suggests a general approach for estimating the trend of a univariate time series. It begins by suggesting and defining a set of "desirable" trend properties, namely "Fidelity", "Smoothness", "Invariance" and "Additivity", which are then incorporated into the design of an appropriate non-stationary time series model. The unknown parameters of the model are then estimated using a wide selection of "optimal" procedures, each parameter having at least two such procedures applied to it. Attention is paid to the development of algorithms to implement the procedures in practice. The model is gradually extended from a basic, non-seasonal model consisting of a simple lagged trend to a general, seasonal model incorporating a variable parameter, general autoregressive trend.

The Ozark National Scenic Riverways (ONSR) has seen a wide range of recreationists since its establishment in 1964. To better understand these recreationists, Experience Use History (EUH) has been identified as a measure of exposure to ONSR. EUH is a construct used to identify how often a visitor frequents an area, as well as his or her history with that area. The purpose of this study is to identify the changes in river user demographics over the past 24 years and how these changes relate to EUH using time series analyses. Study field methods follow procedures of Rapid Assessment Visitor Inventory (RAVI), which has been periodically conducted at ONSR since 1972. This study utilizes questionnaires for which raw data were available, collected from 1986 through 2010 to conduct time series analyses of visitor demographics and EUH. EUH groupings were created using the methods of Smith et al. (2009), resulting in the identification of Casual Newcomers, Casual Veterans, Occasional, and Frequent visitors. While a wealth of information exists pertaining to EUH and general demographics, there is an absence of studies analyzing the dynamic relationship of these variables over time. This research examines for the first time if EUH classifications are also time-dependent or if they are functionally stable at a quarter-century time scale. EUH trends over time showed that Casual Newcomers have begun to transition into Casual Veterans throughout the riverways as their history and frequency of visits increased over the years. Changes in age, watercraft type, and gender were also detected resulting in an aging visitor group to ONSR, an increase in motorboat use, and a slow increase in the proportion of female visitors. Experiences, preferences, and attitudes were also analyzed in terms of both EUH and river district. Finally, average distance traveled by visitors was also analyzed showing ONSR to be a regional attraction attended by both urban and nonurban visitors. Overall this study suggests that visitor populations to ONSR are dynamic. Some of the most important findings of this study showed that EUH category for a given visitor can and does change over time. The Casual Newcomers and Casual Veterans had clear trends; all three river districts showed distinct trends of increasing Casual Veterans and decreasing Casual Newcomers over time but at varying rates of change in proportion. This finding coupled with the aging of visitors to ONSR, suggests that visitors are returning to the riverways and, over time, increasing in experience, thus transforming the Casual Newcomers into Casual Veterans.