Due to its usefulness in various social contexts, from Intelligent Transportation Systems (ITSs) the reduction of urban pollution, road traffic prediction represents an active research area scientific community, with strong potential impact on citizens’ well-being. Already considered a non-trivial problem, many real applications additional level complexity is given by large amount data requirin...

[1] How well can the extent of arctic sea ice be predicted for lead periods of up to one year? The forecast ability of a linear empirical model is explored. It uses as predictors historical information about the ocean and ice obtained from an ice–ocean model retrospective analysis. The monthly model fields are represented by a correlation-weighted average based on the predicted ice extent. The ...

This study creates an adaptive procedure for sequential forecasting of incident duration. This adaptive procedure includes two adaptive Artificial Neural Network-based models as well as the data fusion techniques to forecast incident duration. Model A is used to forecast the duration time at the time of incident notification, while Model B provides multi-period updates of duration time after th...

U the spatial distribution of geological deposits that are capable of containing buried and relatively intact archaeological sites is important for archaeological research and CRM (Eckerle et al. 2000; Horne et al. 2001; Monaghan and Lovis 2005; Stafford 1995). This chapter describes a methodology for modeling and visualizing large landscapes in a way that forecasts settings with potential to c...

This paper examines the forecasting ability of the dividend-price ratio for international stock market returns. Hitherto, existing research has only considered this issue in-sample and in a linear framework. Hence, this paper provides the first systematic study of non-linear forecasting within the present value model context. Using an asymmetric variant of the popular ESTR model we demonstrate ...

Motivated by the promising results of global-scale ensemble forecasting, a number of groups have attempted mesoscale, short-range ensemble forecasting (SREF), focusing mainly over the eastern half of the United States. To evaluate the performance of mesoscale SREF over the Pacific Northwest and to test the value of using different initial analyses as a means of ensemble forecast generation, a f...

Recently, a prototype dengue early warning system was developed to produce probabilistic forecasts of dengue risk three months ahead of the 2014 World Cup in Brazil. Here, we evaluate the categorical dengue forecasts across all microregions in Brazil, using dengue cases reported in June 2014 to validate the model. We also compare the forecast model framework to a null model, based on seasonal a...

A serious problem in the initialization of a climate forecast model is the model-data incompatibility caused by systematic model biases. Here we use the Lamont model to demonstrate that these biases can be effectively reduced with a simple statistical correction, and the bias-corrected model can have a more realistic internal variability as well as an improved forecast performance. The results ...

T paper investigates the capacity investment decision of a supplier who solicits private forecast information from a manufacturer. To ensure abundant supply, the manufacturer has an incentive to inflate her forecast in a costless, nonbinding, and nonverifiable type of communication known as “cheap talk.” According to standard game theory, parties do not cooperate and the only equilibrium is uni...

Data quality affects decision quality. In any forecasting and estimation model, the quality of the forecast data or the estimated data needs to be evaluated before using for decision-making. In two-level supply chain model, the retailer collects the sales information and use it to forecast the future demands over the lead-time, and the manufacturer collects the information about retailer's orde...