The increasing availability of large amounts of historical data and the need of performing accurate forecasting of future behavior in several scientific and applied domains demands the definition of robust and efficient techniques able to infer from observations the stochastic dependency between...

The evolutionary design of time series forecasters is a field that has been explored for several years now. In this paper, a complete design and training of ARMA (Auto-Regressive Moving Average) and ANN (Artificial Neural Networks) models through the use of Evolutionary Computation is presented....

In general, due to inherently high complexity, carbon prices simultaneously contain linear and nonlinear patterns. Although the traditional autoregressive integrated moving average (ARIMA) model has been one of the most popular linear models in time series forecasting, the ARIMA model cannot...

We forecast high-resolution solar irradiance time series using an exponential smoothing state space (ESSS) model. To stationarize the irradiance data before applying linear time series models, we propose a novel Fourier trend model and compare the performance with other popular trend models...

Due to the unique features of nuclear energy market, this paper tries to propose a novel data-characteristic-driven modeling methodology based on the principle of “data-characteristic-driven modeling”, aiming at formulating appropriate forecasting model closely in terms of sample data’s...

This contribution presents the application of a portfolio of forecasters to the problem of wind speed forecasting. This portfolio is created using a single time series and it is based on a number of time series characteristics, previously proposed, and a set of novel time series features. The...

A procedure to estimate the capacity of minihydro plants based on water flow time series forecasting is presented. First, the classic method used for this purpose is introduced and then a set of methodologies to assess the feasibility of minihydro generation and to determine system capacity...

We propose in this paper an original technique to predict global radiation using a hybrid ARMA/ANN model and data issued from a numerical weather prediction model (NWP). We particularly look at the multi-layer perceptron (MLP). After optimizing our architecture with NWP and endogenous data...

In this work we introduce the forecasting model with which we participated in the NN5 forecasting competition (the forecasting of 111 time series representing daily cash withdrawal amounts at ATM machines). The main idea of this model is to utilize the concept of forecast combination, which has...