Sales forecasting is one of the most important issues in managing information technology (IT) chain store sales since an IT chain store has many branches. Integrating feature extraction method and prediction tool, such as support vector regression (SVR), is a useful method for constructing an effective sales forecasting scheme. Independent component analysis (ICA) is a novel feature extraction ...

This study aims to analyze the effects of data pre-processing on the forecasting performance of neural network models. We use three different Artificial Neural Networks techniques to predict tourist demand: multi-layer perceptron, radial basis function and Elman neural networks. The structure of the networks is based on a multiple-output approach. We use official statistical data of inbound int...

I use the adaptive elastic net in a Bayesian framework and test its forecasting performance against lasso, adaptive lasso and elastic net (all used in a Bayesian framework) in a series of simulations, as well as in an empirical exercise for macroeconomic Euro area data. The results suggest that elastic net is the best model among the four Bayesian methods considered. Adaptive lasso, on the othe...

Reliable and accurate time-series forecasting is critical in many fields including energy, finance, and manufacturing. Many time-series tasks, however, suffer from a limited amount of training data (i.e., the cold start problem) resulting in poor forecasting performance. Recently, convolutional neural networks (CNNs) have shown outstanding image classification performance even on tasks with sma...

The purpose of this work is to present a simple global solar irradiance forecasting framework based on the optimization of the k-nearest-neighbors (kNN) and artificial neural networks algorithms (ANN) for time horizons ranging from 15 min to 2 h. We apply the proposed forecasting models to irradiance from five locations and assessed the impact of different micro-climates on forecasting performa...

Mediumand long-term runoff forecasting is essential for hydropower generation and water resources coordinated regulation in the Yellow River headwaters region. Climate change has a great impact on runoff within basins, and incorporating different climate information into runoff forecasting can assist in creating longer lead-times in planning periods. In this paper, a multimodel approach was dev...

Successful river flow forecasting is a major goal and an essential procedure that is necessary in water resource planning and management. There are many forecasting techniques used for river flow forecasting. This study proposed a hybrid model based on a combination of two methods: Self Organizing Map (SOM) and Least Squares Support Vector Machine (LSSVM) model, referred to as the SOM-LSSVM mod...

Achieving relatively high-accuracy short-term wind speed forecasting estimates is a precondition for the construction and grid-connected operation of wind power forecasting systems for wind farms. Currently, most research is focused on the structure of forecasting models and does not consider the selection of input variables, which can have significant impacts on forecasting performance. This p...

In recent years, Artificial Intelligence (AI) methods have proven to be successful tools for forecasting in the sectors of business, finance, medical science and engineering. In this study, we utilize a Genetic Algorithm (GA) to select the optimal variable weights in order to predict exchange rates using Genetic Algorithms, Particle Swam Optimization (PSO) and Back Propagation Network (BPN). Th...

The results presented in this paper are part of a research project to investigate the possibilities to apply massively parallel architectures for numerical weather forecasting. Within numerical weather forecasting several numerical techniques are used to solve the model equations. This paper compares the performance of implementations on a MasPar system of two techniques, nite di erence and spe...