Prediction interval estimation for wind farm power generation forecasts using support vector machines

Accurate forecasting of wind power generation is quite an important as well as challenging task for the system operators and market participants due to its high uncertainty. It is essential to quantify uncertainties associated with wind power generation forecasts for their efficient application in optimal management of wind farms and integration into power systems. Prediction intervals (PIs) are well known statistical tools which are used to quantify the uncertainty related to forecasts by estimating the ranges of the future target variables. This paper investigates the application of a novel support vector machine based methodology to directly estimate the lower and upper bounds of the PIs without expensive computational burden and inaccurate assumptions about the distribution of the data. The efficiency of the method for uncertainty quantification is examined using monthly data from a wind farm in Australia. PIs for short term application are generated with a confidence level of 90%. Experimental results confirm the ability of the method in constructing reliable PIs without resorting to complex computational methods.

Improving the quality of load forecasts using smart meter data

For the operator of a power system, having an accurate forecast of the day-ahead load is imperative in order to guaranty the reliability of supply and also to minimize generation costs and pollution. Furthermore, in a restructured power system, other parties, like utility companies, large consumers and in some cases even ordinary consumers, can benefit from a higher quality demand forecast. In this paper, the application of smart meter data for producing more accurate load forecasts has been discussed. First an ordinary neural network model is used to generate a forecast for the total load of a number of consumers. The results of this step are used as a benchmark for comparison with the forecast results of a more sophisticated method. In this new method, using wavelet decomposition and a clustering technique called interactive k-means, the consumers are divided into a number of clusters. Then for each cluster an individual neural network is trained. Consequently, by adding the outputs of all of the neural networks, a forecast for the total load is generated. A comparison between the forecast using a single model and the forecast generated by the proposed method, proves that smart meter data can be used to significantly improve the quality of load forecast.

On the fringe: barriers to participation in the new economy in regional areas

Electronic commerce offers tools which potentially support the development and re·invigoration of regional economies because of their ability to /ink enterprises and labour markets in networks across dispersed geographical areas. However. the evidence that regional areas of Australia benefit from this development in accordance with optimistic forecasts of the potential is, at best, mixed. This paper examines the constraints on the development of IT-based activity in regional areas and identifies barriers to full participation of those areas in Australia. It argues that regional areas are unlikely to benefit from the 'new economy' without a major changes in government thinking about regional development.

Promise more, leave less

Following Kim and Ritter (1999) who find that earnings forecasts provide more accurate valuations of IPOs, this paper analyses whether the owners of companies seeking to list will leave less money on the table if positive dividend per share (DPS) yield forecasts are made in the prospectus. Our findings indicate that DPS yield forecasts by directors of Industrial company IPOs have been an important ingredient in the amount of money left on the table. A similar result is found for Limited Liability IPOs and those that do not offer options to subscribers to buy more shares. The offer of an operational dividend reinvestment plan in the prospectus does not appear significant in reducing the amount of money left on the table.

Globalisation and manufacturing strategy in the TCF industry

Purpose – The textiles, clothing, and footwear (TCF) industry has struggled in Australia since the government commenced dismantling tariffs. By sourcing from Asia, middlemen undercut established suppliers, and retail chains set benchmark low prices with their imported “house” labels. The policy-makers predicted that local producers would become more efficient, and export to make up for lost sales, but the media paints a picture of rising imports, retrenchments, and factory closures. The research objective was to discover what strategies the survivors (actually) employ in adapting to the pressures of globalisation.

Design/methodology/approach – More than 30 companies were involved in the study, ranging from small family businesses to subsidiaries of big multinationals. Each case study was based on an interview with a senior executive, normally followed by a plant tour. This methodology suits a fresh topic, as it avoids preconceptions and imposes no bounds.

Findings – Results show that the policy change was based on “pie in the sky” forecasts. Increasingly, TCF production is transferred to cheap offshore locations, generally via subcontracting plus the “badging” of foreign designs. To survive, local factories should focus on quality and customer service, preferably in niche markets (like uniforms), or for specific customer groups, and develop technologically advanced products. A move down the supply chain into retailing can also assist. Large multinational corporations that engage in foreign direct investment dominate the management literature.

Originality/value – This paper presents a different perspective, neglected in international operations management, whereby domestically oriented businesses attempt to defend themselves against the adverse consequences of globalisation.

The use of injury surveillance databases to identify emerging injury hazards

Among the many valuable uses of injury surveillance is the potential to alert health authorities and societies in general to emerging injury trends, facilitating earlier development of prevention measures. Other than road safety, to date, few attempts to forecast injury data have been made, although forecasts have been made of other public health issues. This may in part be due to the complex pattern of variance displayed by injury data. The profile of many injury types displays seasonality and diurnal variance, as well as stochastic variance. The authors undertook development of a simple model to forecast injury into the near term. In recognition of the large numbers of possible predictions, the variable nature of injury profiles and the diversity of dependent variables, it became apparent that manual forecasting was impractical. Therefore, it was decided to evaluate a commercially available forecasting software package for prediction accuracy against actual data for a set of predictions. Injury data for a 4-year period (1996 to 1999) were extracted from the Victorian Emergency Minimum Dataset and were used to develop forecasts for the year 2000, for which data was also held. The forecasts for 2000 were compared to the actual data for 2000 by independent t-tests, and the standard errors of the predictions were modelled by stepwise hierarchical multiple regression using the independent variables of the standard deviation, seasonality, mean monthly frequency and slope of the base data (R = 0.93, R² = 0.86, F_{(3, 27)} = 55.2, p < 0.0001). Significant contributions to the model included the SD (β = 1.60, p < 0.001), mean monthly frequency (β =  - 0.72, p < 0.002), and the seasonality of the data (β = 0.16, p < 0.02). It was concluded that injury data could be reliably forecast and that commercial software was adequate for the task. Variance in the data was found to be the most important determinant of prediction accuracy. Importantly, automated forecasting may provide a vehicle for identifying emerging trends.

The relevance of demographic variables in assessing student satisfaction: a study of international postgraduate students

Servicing an increasingly diverse international student community in the globally competitive education market is a challenge faced by Australian universities. Market forecasts indicate a seven-fold increase in the number of students seeking higher education overseas by 2025 (Bohm et al, 2002) and with the growing diversity and competitiveness of the industry, universities will need to focus on factors influencing student satisfaction to improve service quality where required.

This paper, using logistic regression, ANOVA, and MANOVA, investigates the influence of country, age and gender with regard to satisfaction among international postgraduate students from four Asian countries studying in universities in Victoria. The results indicate that there is an inverse relationship between age and satisfaction among postgraduate students while the gender of the students does not have an impact on satisfaction, and that significant differences are evident between the four countries investigated with respect to the levels of satisfaction.

Student satisfaction : impact of age and gender on satisfaction among international postgraduate students from Asia

Customer satisfaction is a core-marketing concept. It is considered as a major outcome of marketing activity and serves to link processes, culminating in purchase and consumption with post-purchase phenomena such as attitudinal change, customer retention, repeat purchase, brand loyalty, positive word-of-mouth communication. Student satisfaction is a strategic factor in developing a competitive advantage in the highly attractive and globally competitive international education market. Market forecasts indicate a seven-fold increase in the number of students seeking higher education overseas by 2025 and given the competitiveness of the industry, universities will need to focus on variables influencing student satisfaction in order to address areas where improvements in service quality are required.

A sample of 371 postgraduate students from China, India, Indonesia and Thailand, is investigated. The study highlights the development of a scale to measure international postgraduate student satisfaction. The scale demonstrates the importance of four predominant factors influencing university choices - Education Resources; Communication and Guidance; Customer Value and Study Outcomes; and Image, Prestige and Recognition. Using logistic regression and chi square testing, this paper investigates the impact of age and gender on satisfaction among international postgraduate students from four Asian countries studying in universities in Victoria, Australia, on theses factors. The results indicate that age has a positive relationship with satisfaction among postgraduate students while the influence of gender has no effect on influencing satisfaction among postgraduate students from Asia.

International education services: challenges for Australia

Australian universities face a challenging task of servicing an increasingly diverse international student community in the globally competitive education market. The pressure on universities to successfully negotiate the cultural diversity and improvement in service quality will continue to increase with the global expansion of the international student market. Market forecasts indicate an increase in the number of students seeking higher education overseas by 2025 to 7.2 million. The attractiveness of the international education market in the form of both pecuniary and non pecuniary benefits will continue to create a highly competitive environment for Australia requiring Australian higher education institutions to pursue well planned strategies to maintain a globally competitive position.

Student satisfaction is a key strategic variable in maintaining such a competitive position with long term benefits arising from student loyalty, positive word of mouth (WOM) communication and image of the study destinations and to meet the challenges of increasing global competition, rising student expectations of quality, service and value for money.

This paper, based on the expectancy-disconfirmation paradigm, and using logistic regression, ANOVA and chi square testing, investigates factors that influence international postgraduate students from four Asian countries studying in Australia and concludes with strategic implications for universities

Contemporaneous intra-day volume, option, and futures volatility transmissions across parallel markets

The primary objective of this article is to investigate volatility transmission across three parallel markets operating on the Sydney Futures Exchange (SFE), both within and out of sample. Half-hourly observations are sampled from transaction data for the share price index (SPI) futures, SPI futures options, and 90-day bank accepted bill (BAB) futures markets, and the analysis is carried out using the simultaneous volatility (SVL) system of equations as well as competing volatility models. The results confirm the poor ability of GARCH models to fit intraday data. This study also applies an artificial nesting procedure to evaluate the out-of-sample volatility forecasts. Implied volatility has very limited (if any) predictive power when evaluated in isolation, whereas the SVL model with implied volatility embedded provides incremental information relative to competing model forecasts.

The exchange rate disconnect puzzle: a resolution?

Early empirical studies of exchange rate determinants demonstrated that fundamentals-based monetary models were unable to outperform the benchmark random walk model in out-of-sample forecasts while later papers found evidence in favor of long-run exchange rate predictability. More recent theoretical works have adopted a microeconomic structure; a utility-based new open economy macroeconomic framework and a rational expectations present value model. Some recent empirical work argues that if the models are adjusted for parameter instability, it is a good predictor of nominal exchange rates while others use aggregate idiosyncratic volatility to generate good predictions. This latest research supports the idea that fundamental economic variables are likely to influence exchange rates especially in the long run and further that the emphasis should change to the economic value or utility based value to assess these macroeconomic models.

Do retail option traders know better about market volatility?

This paper examines the informational content and predictive power of implied volatility over different forecasting horizons in a sample of European covered warrants traded in the Hong Kong and Singapore markets. The empirical results show that time-series-based volatility forecasts outperform implied volatility forecast as a predictor of future volatility. The finding also suggests that implied volatility is biased and informationally inefficient. The results are attributable to the fact in Hong Kong and Singapore the covered warrants markets are dominated by retail investors, who tend to use covered warrants' leverage to speculate on the price movements of the underlying rather than to express their view on volatility.

Forecasting inflation, industrial output and exchange rates : a template study for India

This paper reports results from a forecasting study for inflation, industrial output and exchange rates for India. We cannot reject the null hypothesis for linearity for all series used except for the growth rate of the foreign exchange series and our analysis is based on linear models, ARIMA and bivariate transfer functions and restricted VAR. Forecasting performance is evaluated using the models’ root mean-squared error differences and Theil’s inequality coefficients from recursive origin static, fixed origin dynamic and rolling origin dynamic forecasts. For models based on weekly data, based on RMSEs, we find that the bivariate models improve upon the forecasts of the ARIMA model while for models based on monthly data the ARIMA model has almost always better performance. In choosing between the two bivariate models on the basis of RMSEs, our overall results tend to support the use of a restricted VAR, as this model had the best forecasting performance more frequently than the transfer function model.

Monetary models of exchange rates and the random walk

Three alternative monetary models of exchange rate are tested using data on the Italian lira - US doIIar exchange rate. II is shown that up to the early 1990s these economic models perform better than the random walk model in out-of-sample forecasts.

Federally funded aged care provision in regional Victoria : Examining current provision and estimating future infrastructure requirements using existing population-based benchmarks

The following paper examines federally accredited and funded aged care provision in regional Victoria. Benchmarks that have been set by the Australian Department of Health and Ageing, are used to measure and compare the relative number of high and low level aged care positions and Community Aged Care Packages in six regional Victorian centres.
Using population forecasts, the additional aged care positions that each centre will require to meet the provision benchmarks in the year 2021 have been estimated. These figures are then translated into infrastructure requirements for the regional Victorian city of Greater Bendigo. This is done by surveying Greater Bendigo’s existing residential aged care facilities. Strategies for the provision of additional high and low level residential aged care infrastructure are explored using a matrix governed by size and configuration. Variations in these two aspects are shown to affect the location options for future facilities in Greater Bendigo. The implications of the benchmarks are also investigated in terms of facilities for the provision of Community Aged Care Packages.
The research is funded by a double ARC APAI grant between the Built Environment Research Group at Deakin University, The Centre for Sustainable Regional Centres at La Trobe University, the City of Greater Bendigo and the City of Warrnambool.