Independent forecasts obtained from different temporal aggregates of a given time series may not be mutually consistent. State-of-the art forecasting methods usually apply adjustments on the individual level forecasts to satisfy the aggregation constraints. These adjustments require the estimation of the covariance between the individual forecast errors at all aggregation levels. In order to ke...

Artificial neural network theory generally minimises a standard statistical error, such as the sum of squared errors, to learn relationships fiom the presented data. However, applications in business have shown that real forecasting problems require alternative error measures. Errors, identical in magnitude, cause different costs. To reflect this, a set of asymmetric cost functions is proposed ...

Baart, F and van Gelder, P.H.A.J.M and van Koningsveld, M. 2011 Confidence in real-time forecasting of morphological storm impacts. Journal of Coastal Research, SI 64 (Proceedings of the 11th International Coastal Symposium), – . Szczecin, Poland, ISSN 0749-0208 Previous studies have expanded warning systems for coastal predictions with information on coastal morphology. Here we present the met...

We construct a fuzzy varying coefficient bilinear regression model to deal with the interval financial data and then adopt the least-squares method based on symmetric fuzzy number space. Firstly, we propose a varying coefficient model on the basis of the fuzzy bilinear regression model. Secondly, we develop the least-squares method according to the complete distance between fuzzy numbers to est...

The task of forecasting a time series over a long horizon is commonly tackled b y iterating one-step-ahead predictors.Despite the popularity that this approach gained in the prediction communit y, its design is still plagued by a number of important unresolved issues, the most important being the accumulation of prediction errors. We introduce a local method to learn one-step-ahead predictors w...

This research examines and analyses the use of Neural Network Regression (NNR) models in foreign exchange (FX) forecasting and trading models. The NNR models are benchmarked against traditional forecasting techniques to ascertain their potential added value as a forecasting and quantitative trading tool. In addition to evaluating the various models using traditional forecasting accuracy measure...

Load Forecasting plays a key role in making today's and future's Smart Energy Grids sustainable and reliable. Accurate power consumption prediction allows utilities to organize in advance their resources or to execute Demand Response strategies more effectively, which enables several features such as higher sustainability, better quality of service, and affordable electricity tariffs. It is eas...

Selection of appropriate forecasting models with their optimized parameters for a given business scenario is a challenging task and requires reasonable expert knowledge and experience. The problem of selecting the best forecasting model becomes computationally complex when the business needs forecasts on thousands of time series at a given time period. Many a times business users are interested...

Artificial neural network theory generally minimises a standard statistical error, such as the sum of squared errors, to learn relationships from the presented data. However, applications in business have shown that real forecasting problems require alternative error measures. Errors, identical in magnitude, cause different costs. To reflect this, a set of asymmetric cost functions is proposed ...