0957-4174/$ see front matter 2012 Elsevier Ltd. A http://dx.doi.org/10.1016/j.eswa.2012.05.040 ⇑ Corresponding author. Tel.: +90 312 2977900. E-mail address: [email protected] (C.H. Alad In recent years, time series forecasting studies in which fuzzy time series approach is utilized have got more attentions. Various soft computing techniques such as fuzzy clustering, artificial neural net...

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 ...

This paper compares performance of a hidden Markov model (HMM) and a hybrid HMM/ANN model in seismic events modeling. Observation variables are assumed to follow a Poisson distribution. Parameters of the discrete-time two-state models are estimated on the basis of data on seismic events that were recorded in Poland from 1991 to 1995. Then, on the basis of the estimation results, the most likely...

E D P R Demand forecasts play a crucial role for supply chain management. The future demand for a certain product is the basis for the respective replenishment systems. Several forecasting techniques have been developed, each one with its particular advantages and disadvantages compared to other approaches. This motivates the development of hybrid systems combining different techniques and thei...

This paper studies uncertainty using the ECB Survey of Professional Forecasters’ data. We consider both inflation and real GDP growth forecasts at the micro level and explore forecast uncertainty using two alternative measures, i.e. conventional standard deviation of individual point forecasts and the median values of individual forecasters’ uncertainty, which are based on subjective probabilit...

Flood forecasting in rivers and coastal waters demands careful attention both to the reliability of the forecasts and the safety of the decisions made on the basis of the results. Advances in data driven modelling have improved the accuracy of forecasts made using physically based models. The hybrid modelling approach combines the best features of physically based and data driven modelling, eit...

Many researchers have argued that combining many models for forecasting gives better estimates than single time series models. For example, a hybrid architecture comprising an autoregressive integrated moving average model (ARIMA) and a neural network is a well-known technique that has recently been shown to give better forecasts by taking advantage of each model's capabilities. However, this a...

This report discusses problems and methodologies that lie in the broad scope of systems and signals, with special focus on modeling, identification and signal processing; adaptation and learning; discrete event and hybrid systems; and stochastic systems. A common theme underlying all these areas is that problems in control systems and signals are usually defined and best studied in the framewor...

We develop a hybrid, real-time solar forecasting computational model to construct prediction intervals (PIs) of one-minute averaged direct normal irradiance for four intra-hour forecasting horizons: five, ten, fifteen, and 20 min. This hybrid model, which integrates sky imaging techniques, support vector machine and artificial neural network sub-models, is developed using one year of co-located...