Bayesian learning implies decreasing weights on prior beliefs and increasing weights on the accuracy of the analyst’s past forecast record, as the number of forecast errors comprising her forecast record (its length) increases. Consistent with this model of investor learning, empirical tests show that investors’ reactions to forecast news are increasing in the product of the accuracy and length...

Very short-period quantitative precipitation forecast (QPF) or nowcast schemes provide deterministic output that fails to convey explicit measures of the uncertainty in the forecast. Presented here is a forecast methodology based upon a Bayesian hierarchical model that produces a QPF product for a 1-h period along with an associated estimated forecast error field. The precipitation forecast qua...

Sometimes, the best available information about an uncertain future is a single forecast. On the other hand, stochastic-programming models need future data in the form of scenario trees. While a single forecast does not provide enough information to construct a scenario tree, a forecast combined with historical data does—but none of the standard scenario-generation methods is suited to handle t...

Data traffic raises dramatically in recent years. The burst of data demands for high capacity of communication networks. This brings more challenges for the industry. Forecast of traffic growth becomes important. Based on the traffic forecast, the industry can design a long-term strategy for communication development. To evaluate traffic forecast result, proper methodology is required. In this ...

Medium-and-long-term load forecasting plays an important role in energy policy implementation and electric department investment decision. Aiming to improve the robustness and accuracy of annual electric load forecasting, a robust weighted combination load forecasting method based on forecast model filtering and adaptive variable weight determination is proposed. Similar years of selection is c...

BACKGROUND Forecasting of health indicators is of great importance for health policy makers. This study is aimed to forecast some important health indicators like crude death rate (CDR), crude birth rate (CBR), and infant mortality rate (IMR) and total fertility rate (TFR) of Pakistan. METHODS In this retrolective study, annual data of these indicators for years 1984-2010 have been used. The ...

Four dimensional variational (4D-Var) Data Assimilation (DA) method is used to find the optimal initial conditions by minimizing cost function in which background information and observations are provided as the input of the cost function. The corrected initial condition based on background error covariance matrix and observations improve the forecast. The targeted observations determined by us...

Evaluation of forecast optimality in economics and finance has almost exclusively been conducted under the assumption of mean squared error loss. Under this loss function optimal forecasts should be unbiased and forecast errors serially uncorrelated at the single period horizon with increasing variance as the forecast horizon grows. Using analytical results we show that standard properties of o...

Disease transmission forecasts can help minimize human and domestic animal health risks by indicating where disease control and prevention efforts should be focused. For disease systems in which weather-related variables affect pathogen proliferation, dispersal, or transmission, the potential for disease forecasting exists. We present a seasonal forecast of St. Louis encephalitis virus transmis...

Evaluation of forecast optimality in economics and Þnance has almost exclusively been conducted under the assumption of mean squared error loss. Under this loss function optimal forecasts should be unbiased and forecast errors should be serially uncorrelated at the single period horizon with increasing variance as the forecast horizon grows. Using analytical results, we show in this paper that ...