Forecasting is the starting point for drawing good strategies facing the demand variability in the increasingly complex and competitive today's markets. This article discusses two methods of dealing with demand variability in seasonal time series using artificial neural networks (ANN). First a multilayer perceptron model for time series forecasting is proposed. Several learning rules used ...

0950-7051/$ see front matter 2010 Elsevier B.V. A doi:10.1016/j.knosys.2010.07.006 * Corresponding author. Tel.: +886 3 5712121x573 E-mail addresses: [email protected] (Y.-S (L.-I. Tong). The autoregressive integrated moving average (ARIMA), which is a conventional statistical method, is employed in many fields to construct models for forecasting time series. Although ARIMA can be adopte...

The dynamic behavior of the term structure of interest rates is difficult to replicate with models, and even models with a proven track record of empirical performance have underperformed since the early 2000s. On the other hand, survey expectations are accurate predictors of yields, but only for very short maturities. We argue that this is partly due to the ability of survey participants to in...

Forecasting hotel arrivals and occupancy is an important component in hotel revenue management systems. In this paper we propose a new Monte Carlo simulation approach for the arrivals and occupancy forecasting problem. In this approach we simulate the hotel reservations process forward in time, and these future Monte Carlo paths will yield forecast densities. A key step for the faithful emulati...

Recent developments in dynamical seasonal forecasting of potential relevance to agricultural management are discussed. These developments emphasize the importance of using a fully probabilistic approach at all stages of the forecasting process, from the dynamical ocean–atmosphere models used to predict climate variability at seasonal and interannual time scales, through the models used to downs...

In this paper, we analyze the importance of curvature term structure movements on forecasts of interest rates. An extension of the exponential three-factor Diebold and Li (2006) model is proposed, where a fourth factor captures a second type of curvature. The new factor increases model ability to generate volatility and to capture nonlinearities in the yield curve, leading to a significant impr...