Accurate time series forecasting is a key issue to support individual and organizational decision making. In this paper, we introduce novel methods for multi-step seasonal time series forecasting. All the presented methods stem from computational intelligence techniques: evolutionary artificial neural networks, support vector machines and genuine linguistic fuzzy rules. Performance of the sugge...

In this paper, a hybrid model integrating wavelet and least squares support machines (LSSVM) is proposed for crude oil price forecasting. In this model, Haar à trous wavelet transform is first selected to decompose an original time series into several sub-series with different scales. Then the LSSVM is used to predict each sub-series. And the final oil price forecasting is obtained by reconstru...

Abstract This study explores the multi-step ahead forecasting performance of a so-called hybrid conditional quantile method, which combines relevant forecasts from parametric and semiparametric methods. The focus is on lower (left) upper (right) tail quantiles distribution response variable. First, we evaluate compare out-of-sample obtained method five non-hybrid methods, employing large data s...

Accurate demand forecasting is one of the most key issues in inventory management of spare parts. The problem of modeling future consumption becomes especially difficult for lumpy patterns, which characterized by intervals in which there is no demand and, periods with actual demand occurrences with large variation in demand levels. However, many of the forecasting methods may perform poorly whe...

Sub-seasonal to seasonal (S2S) retrospective forecasts from three global coupled models are used to evaluate the predictability of the onset and demise dates of the rainy season over monsoonal regions. The onset and demise dates of the rainy season are defined using only precipitation data. The forecasts of the onset and demise dates of the rainy season are based on a hybrid methodology that co...

Arctic change and reductions in sea ice are impacting Arctic communities and are leading to increased commercial activity in the Arctic. Improved forecasts will be needed at a variety of timescales to support Arctic operations and infrastructure decisions. Increased resolution and ensemble forecasts will require significant computational capability. At the same time, high performance computing ...

46 47 A hybrid 3DVAR-EnKF data assimilation algorithm is developed based on 3DVAR and 48 ensemble Kalman filter (EnKF) programs within the Advanced Regional Prediction System 49 (ARPS). The hybrid algorithm uses the extended alpha control variable approach to combine the 50 static and ensemble-derived flow-dependent forecast error covariances. The hybrid variational 51 analysis is performed usi...

The present paper deals with both the modeling and dynamic control of a solar hybrid thermochemical reactor designed to produce syngas through high-temperature steam gasification biomass. First, model based on thermodynamic equilibrium is presented. Cantera toolbox used. Then, model-based predictive controller (MPC) proposed aim maintaining reactor’s temperature at its nominal value, thus prese...

In the present study Artificial Neural Network (ANN) has been optimized using a hybrid algorithm of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The hybrid GA-PSO algorithm has been used to improve the estimation of electricity demand of the state of Tamil Nadu in India. The ANN-GA-PSO model uses gross domestic product (GSDP); electricity consumption per capita; income growth r...