We propose a framework for out-of-sample predictive ability testing and forecast selection designed for use in the realistic situation in which the forecasting model is possibly misspecified, due to unmodeled dynamics, unmodeled heterogeneity, incorrect functional form, or any combination of these. Relative to the existing literature (Diebold and Mariano (1995) and West (1996)), we introduce tw...

In this paper we explore the hierarchical nature of tourism demand time series and produce short-term forecasts for Australian domestic tourism. The data and forecasts are organized in a hierarchy based on disaggregating the data for different geographical regions and for different purposes of travel. We consider five approaches to hierarchical forecasting: two variations of the top-down approa...

Abstract: Focusing on short-term wind power forecast, a method based on the combination of Genetic Algorithm (GA) and Extreme Learning Machine (ELM) has been proposed. Firstly, the GA was used to prepossess the data and effectively extract the input of model in feature space. Basis on this, the ELM was used to establish the forecast model for short-term wind power. Then, the GA was used to opti...

We compare the Bank of England’s Inflation Report (IR) quarterly forecasts for growth and inflation to real-time forecasts using a variety of statistical forecasting models that have previously been found useful as forecasting benchmarks. These include linear and non-linear univariate models, and VARs. The results reveal the well-known difficulty of forecasting in a stable macroeconomic environ...

In this chapter four combinations of input features and the feedforward, cascade forward and recurrent architectures are compared for the task of forecast tourism time series. The input features of the ANNs consist in the combination of the previous 12 months, the index time modeled by two nodes used to the year and month and one input with the daily hours of sunshine (insolation duration). The...

Researchers have adopted online data such as search query volumes to forecast tourism demand for a destination, including tourist volumes and hotel occupancy. However, the massive yet highly correlated query data pose challenges when researchers attempt to include them in the forecasting model. We propose a framework and procedure for creating a composite search index adopted in a generalized d...

We have recently developed a hierarchical K-Means clustering method for weather images. Using this technique, it is possible to identify storms at different scales. In this paper, we will describe an error-minimization technique to identify movement between successive frames of a sequence and show that we can use the K-Means clusters as the minimization kernel. Using this technique in combinati...