Financial time series including high frequency structures like jumps, spikes and stochastic volatility are usually modeled in an ad-hoc manner by stochastic differential equations together with Levy processes. Estimating the parameters and determining the jump size distributions do not have a precise and universally accepted method. Under these circumstances, complexities and confusions usually arise. This book, approaches to this issue from a very different angle through introducing an autocorrelation one process together with finite number of Fourier series terms. Introduction of Fourier series to estimate the dynamics of the process is not done in an ad-hoc manner or as done before in dealing with seasonality. Here the moving average is transformed to a “moving and fluctuating” average by the help of Fourier series. Instead of adding jump structures to the model which makes the parameter estimation quite cumbersome, our model in discrete time can easily be transformed to a well-known mean reverting continuous time process. Moreover, our alternative model turned out to be a quite powerful and accurate forecasting technique.

Intense competition, fast changing technology, increased and diverse customer demand, and fast and reliable services and goods delivery expectations have reduced the profit margins for products. To be able to obtain a sustainable economic success, companies search for ways to offer the existing products via innovative ways. Revenue management is a tool that helps firms in this purpose. On the other hand, to match and even exceed customer anticipations, firms need to understand customer preferences and offer products accordingly. Customers can now reach wide range of competitor products due to online services. Hence it became much more important to apprehend and integrate customer choices into decision support tools. At this point, choice models are used to mathematically understand the customer choice preferences among a set of presented alternatives. This book combines revenue management and choice models to present a methodology to understand customer choices and integrate these to revenue management methods to be able to offer the best set of products to the customers. Also real life applications for hotels have been presented for different methods that are discussed and also proposed in this book with respective algorithms. When combined with decision support tools, concepts presented in this book offers a significant revenue increase for companies.