In this paper a new Back-propagation algorithm appropriately studied for modelling air pollution time series is proposed. The underlying idea is that of modifying the error definition in order to improve the capability of the model to forecast episodes of poor air quality. In the paper five different expressions of error definition are proposed and their performances are rigorously evaluated in...

Stock market is affected by news and information. If the stock market is not efficient, the reaction of stock price to news and information will place the stock market in overreaction and under-reaction states. Many models have been already presented by using different tools and techniques to forecast the stock market behavior. In this study, the reaction of stock price in the stock market was ...

This paper explores the use of flow length and travel time as a pre-processing step for incorporating spatial precipitation information into Artificial Neural Network (ANN) models used for river flow forecasting. Spatially distributed precipitation is commonly required when modelling large basins, and it is usually incorporated in distributed physically-based hydrological modelling approaches. ...

I this paper we extend forecast band evolution and capacitated production modelling to the multiperiod demand case. In this model, forecasts of discrete demand for any period are modelled as bands and defined by lower and upper bounds on demand, such that future forecasts lie within the current band. We develop heuristics that utilize knowledge of demand forecast evolution to make production de...

This paper presents the forecast of electricity consumption based on mathematical models by using the available historical consumption, with daily resolution, including upscaling, applying a hybrid model that incorporates multiple linear regression with artificial neural networks. Therefore, the hybrid model exploits both the unique features of the regression model and of the artificial neural ...

We report on a novel forecasting method based on nonlinear Markov modelling and canonical variate analysis, and investigate the use of a prediction algorithm to forecast conditional volatility. In particular, we assess the dynamic behaviour of the model by forecasting exchange rate volatility. It is found that the nonlinear Markov model can forecast exchange rate volatility significantly better...

Background Ghana is implementing activities towards universal health coverage (UHC) as well as the attainment of the health-related Sustainable Development Goals (SDGs) by the health sector by the year 2030. Aside lack of empirical forecast of the required healthcare facilities to achieve these mandates, health workforce deficits ar...