Time series forecasting is an important area of forecasting in which past observations of the same variable are … models, autoregressive (AR) techniques, moving averages (MA) etc. Recent research activities in forecasting also suggested … that artificial neural networks can be used as an alternative to traditional linear forecasting models. This study will …

activity. The objective of this research is to develop a distribution short-term load forecasting technology consisting of a … forecasting method, development methodology, theories necessary to support required technical components, and the hardware and …

in stock forecasting. Neural networks are viewed as one of the more suitable techniques. In this study, an experiment on … the forecasting of the Stock Exchange of Thailand (SET) was conducted by using feedforward backpropagation neural networks … neural network models in the forecasting of SET. Several global and local factors influencing the Thai stock market were used …

In this paper we present an analysis of the results of a study into wholesale (spot) electricity price forecasting … accuracy of electricity price forecasting models. The efficiency of NN and SVM retraining for price forecasting was evaluated … December 1998. The analysis of the results showed that SVMs with one unique solution, produce more consistent forecasting …

des Marktes hindeuten:¨ Das Kreditrisiko und dessen Portfolio-orientiertes Management wird auch in Zukunft immermehr an …

behaviors in any given situation. This work presents several scalable frameworks for modeling and forecasting agent behavior …, particularly in the realm of international security dynamics. A probabilistic logic formalism for modeling and forecasting behavior … of this problem, forecasting methods are also introduced that operate directly on time series data, rather than an …

recommend that the Agency adopt this composite forecasting method, that uses time series analysis and current National …