Automatic forecasts of large numbers of univariate time series are often needed in business. It is common to have over one thousand product lines that need forecasting at least monthly. In these circumstances, an automatic forecasting algorithm is an essential tool. Automatic forecasting algorithms must determine an appropriate time series model, estimate the parameters and compute the forecast...

Neural networks are good at classification, forecasting and recognition. They are also good candidates of financial forecasting tools. Forecasting is often used in the decision making process. Neural network training is an art. Trading based on neural network outputs, or trading strategy is also an art. We will discuss a seven-step neural network forecasting model building approach in this arti...

This paper introduced the feature of distribution network and rough set theory. The application in power system was elaborated, such as on load forecasting, fault diagnosis, system-state analysis and data mining. Then given an illustration that using RS attribute reduction algorithm obtain the correlative factors in distribution system load forecasting. These factors were input vector of neural...

1 Introduction This Recommendation is the first in a series of three Recommendations that cover international telecommunications forecasting. In the operation and administration of the international telephone network, proper and successful development depends to a large degree upon estimates for the future. Accordingly, for the planning of equipment and circuit provision and of telephone plant ...

A major source of risk in project management is inaccurate forecasts of project costs, demand, and other impacts. The paper presents a promising new approach to mitigating such risk based on theories of decision-making under uncertainty, which won the 2002 Nobel Prize in economics. First, the paper documents inaccuracy and risk in project management. Second, it explains inaccuracy in terms of o...

This paper proposes the combined forecasting model which study on the classic swine fever (CSF) morbidity, using the forecasting results of ARIMA and GM (1, 1) model as the inputs of the majorizing BP neural network. Analyzing the monthly data from 2000 to 2009 and the accuracy of the forecasting results is 97.379%, more accurate and more steady than traditional methods. This research provides ...

• short-term inclement weather forecasting (for hurricanes, etc.), • contaminant plume forecasting in both urban environments (for coordinating emergency response) and battlefield environments (for coordinating troop movements), • long-term ocean current forecasting (for El Niño, climate change, etc.), and • MHD/plasma forecasting (for sunspot cycles, over terms of years, in order to plan space...

Streamflow forecasting is very important for water resources management and flood defence. In this paper two forecasting methods are compared: ARIMA versus a multilayer perceptron neural network. This comparison is done by forecasting a streamflow of a Mexican river. Surprising results showed that in a monthly basis, ARIMA has lower prediction errors than this Neural Network. Key-Words: Auto re...

In this paper we investigate the forecasting performance of the non-linear time series SETAR model by using Canadian GDP data from 1965 to 2000. Besides the with-insample fit, the forecasting performance of a standard linear ARIMA model for the same sample has also been generated for comparative purposes. Two forecasting methods, 1step-ahead and multi-step-ahead forecasting are compared for eac...

Forecasting models have wide applications in decision making. In the real world, rapid changes normally take place in different areas, specifically in financial markets. Collecting the required data is a main problem for forecasters in such unstable environments. Forecasting methods such as Auto Regressive Integrated Moving Average (ARIMA) models and also Artificial Neural Networks (ANNs) need ...