Uncertainty in the capital market means the difference between the expected values ​​and the amounts that actually occur. Designing different analytical and forecasting methods in the capital market is also less likely due to the high amount of this and the need to know future prices with greater certainty or uncertainty. In order to capitalize on the capital market, investors have always sough...

Title of dissertation: Mining of Business Data Shu Zhang, Doctor of Philosophy, 2009 Dissertation directed by: Associate Professor Wolfgang Jank AMSC and R.H.Smith School of Business Applying statistical tools to help understand business processes and make informed business decisions has attracted enormous amount of research interests in recent years. In this dissertation, we develop and apply ...

We consider the design and estimation of quadratic term structure models. We start with a list of stylized facts on interest rates and interest rate derivatives, classified into three layers: (1) general statistical properties, (2) forecasting relations, and (3) conditional dynamics. We then investigate the implications of each layer of property on model design and strive to establish a mapping...

In recent years, wind energy has a remarkable growth in the world, but one of the important problems of power generated from wind is its uncertainty and corresponding power. For solving this problem, some approaches have been presented. Recently, the Artificial Neural Networks (ANN) as a heuristic method has more applications for this propose. In this paper, short-term (1 hour) and mid-term (24...

To further enhance the accuracy and precision of DRAM price forecasting, a hybrid fuzzy and neural approach with virtual experts and partial consensus is proposed. In the proposed methodology, some virtual experts form a committee. These virtual experts construct their own fuzzy linear regression (FLR) equations to forecast the price of a DRAM product from various viewpoints. Each FLR equation ...

Motivated by the increasing integration among electricity markets, in this paper we propose three different methods to incorporate market integration in electricity price forecasting and to improve the predictive performance. First, we propose a deep neural network that considers features from connected markets to improve the predictive accuracy in a local market. To measure the importance of t...

This paper summarizes what we have learned about price stability since the C.D. Howe Institute and Bank of Canada conferences of 1996 and 1997.1 The range of issues that a central bank must consider in its evolving approach to price stability, and that these two conferences covered, is broad. It includes setting targets for the behaviour of the price level, developing techniques for forecasting...

Abstract. In this paper we propose a new forecasting methodology that comprises simultaneous level-wise modeling in the wavelet domain. The WAW methodology (short for wavelet-armax-winters) uses three modeling startegies: ARMAX models capable of incorporating external inputs and model feedbacks, trigonometric regressions sensitive to seasonality effects and Holt-Winters models describing trends...