Energy price forecast is the key information for generating companies to prepare their bids in the electricity markets. However, this forecasting problem is complex due to nonlinear, non-stationary, and time variant behavior of electricity price time series. Accordingly, in this paper a new strategy is proposed for electricity price forecast. The forecast strategy includes Wavelet Transform (WT...

the purpose of this research is to investigate the relationship between earnings variabilityand earnings forecast using neural networks in companies listed on tehran stock exchange. theresearch is of the library and analytical-causal study type, and is based on panel data analysis andneural networks. in this research, financial information of 98 companies from 19 industries intehran stock excha...

Ocean forecasting and oil spill modelling and tracking are complex activities requiring specialised institutions. In this work we present a lighter solution based on the Operational Ocean Forecast Python Engine (OOFε) and the oil spill model General NOAA Operational Modelling Environment (GNOME). These two are robust relocatable and simple to implement and maintain. Implementations of the opera...

This site report describes the HIRLAM project, which deals with the development and maintenance of a state-of-the-art operational numerical short-range weather forecasting system. The report provides an insight into the diierent components of these systems, presents examples what can be achieved, and describes brieey what kind of CSE research is carried out. The international project: Its aim a...

The atmospheric extra-tropical flow is characterized as chaotic motions that are sensitive to initial conditions and thus is merely predictable by operational weather forecast models 2 weeks in advance given the current observational and modelling accuracy (e.g., Lorenz 1969; Leith 1971; Tribbia and Baumhefner 2004). On the other hand, the seasonal and inter-annual variations in the extra-tropi...

We argue that adversarial risk analysis may be incorporated into the structured expert judgement modelling toolkit for cases in which we need to forecast actions of competitors based on knowledge. This is relevant areas such as cybersecurity, security, defence and business competition. As a consequence, present approach facilitate elicitation probabilities over other intelligent agents by decom...

We explain that revisions to successive density forecasts of the same outcome, as measured by the Kullback-Leibler Information Criterion, need not be unpredictable, unlike those to conditional mean forecasts, even when the forecaster uses information efficiently. However one can still test the efficiency of fixed-event conditional density forecasts, similarly to conditional mean forecasts, by t...

In the paper a model to predict the concentrations of particulate matter PM10, PM2.5, SO2, NO, CO and O3 for a chosen number of hours forward is proposed. The method requires historical data for a large number of points in time, particularly weather forecast data, actual weather data and pollution data. The idea is that by matching forecast data with similar forecast data in the historical data...