In the past, a lot of studies about the comparison of exchange rate forecasting models have been carried out. Most of these studies have a similar result which is the random walk model has the best forecasting performance. In this thesis, I want to find a model to beat the random walk model in forecasting the exchange rate. In my study, the vector autoregressive model (VAR), restricted vector autoregressive model (RVAR), vector error correction model (VEC), Bayesian vector autoregressive model are employed in the analysis. These multivariable time series models are compared with the random walk model by evaluating the forecasting accuracy of the exchange rate for three North European countries both in short-term and long-term. For short-term, it can be concluded that the random walk model has the best forecasting accuracy. However, for long-term, the random walk model is beaten. The equal accuracy test proves this phenomenon really exists.

The intraseasonal Oscillation (ISO) is a very strong and coherent mode of variability observed in the Earths climate. Rainfall variability in the intraseasonal timescale is particularly strong in the Tropics and it directly interacts with the South Asian monsoon during boreal summer and with the Australian monsoon during winter. A detailed analysis of the horizontal and vertical structure of the ISO during both summer and winter is presented in this work considering the coupled ocean-atmosphere system. In addition, the role of the intraseasonal variability of the Southeast Asian monsoon is studied in detail. From the applications point of view, the intraseasonal time scale is arguably the most important period of variability. However, extended forecasting of intraseasonal activity has proven to be a difficult task for the state of the art numerical models. In order to improve the forecasts of the ISO activity over the Southeast Asian monsoon region, a physically based empirical scheme was designed. The scheme uses wavelet banding to separate the predictand and predictors into physically significant bands where linear regression followed by recombination of the bands is used to generate the forecast. Results of the empirical scheme suggest that isolating the evolution of the intraseasonal signal from higher frequency variability and noise improve the skill of the prediction. The hypothesis is that a similar phenomenon occurs in numerical models: The strong intraseasonal signal is eroded by high frequency errors due to the model parameterizations, especially in convection. To evaluate the hypothesis, a coupled ocean-atmosphere model was run in ensemble mode for 30 day periods initialized daily for 20 days before to 20 days after major intraseasonal oscillations, allowing the examination of the skill of the model relative to the phase of the oscillation. The results, which confirm the previous hypothesis, represent well the observations for about 7 days after which the magnitude of the errors is greater than the signal itself. An integration scheme was developed for the coupled ocean-atmosphere general circulation model in order to mimic the philosophy of the empirical scheme and use for 30-day forecasts. The propagation features associated to ISO activity are improved.

Thesis (Ph. D.)--University of Hawaii at Manoa, 2005. / Includes bibliographical references (leaves 181-193).

Thesis (M.S. in Meteorology and Physical Oceanography)--Naval Postgraduate School, September 2003. / Thesis advisor(s): Wendell A. Nuss, David S. Brown. Includes bibliographical references (p. 65-66). Also available online.

Thesis (D.Tech degree in Information Technology)--Tshwane University of Technology, 2012. / In this study an interactive scenario analysis technique was developed and tested to forecast e-skills development in South Africa. The developed technique integrates qualitative and quantitative assumptions to improve the forecasting process. Improving the forecasting process is generally an open discourse, given that many organizations use techniques that were rejected from an academic perspective. The extant scenario analysis techniques that integrated quantitative and qualitative assumptions often will trade-off participation of decision makers and rely on experts. The conception of this work was based upon the preliminary analysis conducted on the strengths and weaknesses encountered in the previous forecasting studies. The goal was to evolve a new technique that demonstrates how scenario-based forecasting process can be practically improved by the inclusion of constructs that promote a comprehensive participatory model. The integration of constructs to support a team of decision makers and experts to engage in a participatory forecasting process has demonstrated considerable improvement and acceptance of results by decision makers. The cardinal contributions of this study are the merging of qualitative intuitive logic and quantitative cross-impact analysis to improve forecasting process, the quantification of scenarios to better convey meaningful information and the unique application of the developed technique to forecast demand-supply of e-skills development in South Africa.

published_or_final_version / Statistics / Master / Master of Social Sciences

published_or_final_version / abstract / toc / Economics and Finance / Master / Master of Economics

In current work problems and requirements for demand forecasting in commercial or manufacturing enterprises are analyzed and suitable forecasting algorithms are proposed. In enterprises with multidimensional and heterogeneous demand it is advisable to use different algorithms for different demand constituents and to readjust parameters used for forecasting. Existing forecasting packages are not practical as they are not integrated with commodities or materials supply orders management activities and business processes of enterprise. The orders management system is developed with forecasting component using adopted time series forecasting techniques such as moving average, exponential smoothing, double exponential smoothing, seasonal etc. These techniques ensure reliable forecasting results for different time series models: random, trend, seasonal and cycling, and are integrated with other business management activities. It is possible to calculate deviations of forecasted demand from factual values, to select algorithms giving minimal perсentage error, and to adjust algorithms parameters to changing demand. The system can help managers to choose forecasting algorithms and to adapt their parameters in the course of time. The system is designed using UML CASE tool and implemented in Microsoft .Net environment using MS SQL Server for data storage.

Reservoir systems are subject to several uncertainties that are the result of imperfect knowledge about system behavior and inputs. A major source of uncertainty arises from the inability to predict future inflows. Fortunately, it is often possible to generate probabilistic forecasts of inflow volumes in the form of probability density functions or ensembles. These inflow forecasts can be coupled with stochastic management models to determine reservoir release policies and provide stakeholders with meaningful information of upcoming system responses such as reservoir levels, releases, flood damage risks, hydropower production, water supply withdrawals, water quality conditions, navigation opportunities, and environmental flows, among others. This information on anticipated system responses is also expressed in the form of forecasts that must reliably represent the actual system behavior when it eventually occurs. The first part of this study presents an assessment methodology that can be used to determine the consistency of ensemble forecasts through the use of relative frequency histograms and minimum spanning trees (MST). This methodology is then used to assess a management model's ability to produce reliable ensemble forecasts. It was found that neglecting to account for hydrologic state variables and improperly modeling the finite management horizon decrease ensemble consistency. Several extensions to the existing management model are also developed and evaluated. The second portion of this study involves the management of the uncertainties in reservoir systems. Traditional management models only find management policies that optimize the expected values of system benefits or costs, thereby not allowing operators and stakeholders to explicitly explore issues related to uncertainty and risk management. A technique that can be used to derive management policies that produce desired probabilistic distributions of reservoir system outputs reflecting stakeholder preferences is developed. This technique can be embedded in a user-interactive framework that can be employed to evaluate the trade-offs and build consensus in multi-objective and multi-stakeholder systems. The methods developed in this dissertation are illustrated in case studies of real reservoir systems, including a seven-reservoir, multi-objective system in California's Central Valley.

This study specifically deals with finding a pragmatic solution to the problem of sales forecasting and demand planning in a very dynamic industry, the dairy industry, in the fast moving consumer goods (FMCG) market. Two projects surrounding the Parmalat supply chain were commissioned, the first dealing with sales forecasting, and the second dealing with distribution replenishment planning. This dissertation handles the former and sought to find solutions and integrate the strategic or long-term planning process with the operational forecasting process, and effectively integrate both these into the Parmalat supply chain management process. Of great importance to us during the project was the organizations maturity and level of business discipline currently prevalent, you would therefore constantly find reference to improvements required in other business process in support of a more sophisticated world class Supply Chain Management (SCM) system. / Thesis (MBA)-University of KwaZulu-Natal, 2005.