While engineers have been quantifying rainfall-runoff processes since the mid-19th century, it is only in the last decade that artificial neural network models have been applied to the same task. This paper evaluates two neural networks in this context: the popular multilayer perceptron (MLP), and the radial basis function network (RBF). Using six-hourly rainfall-runoff data for the River Yangt...

The electricity consumption of a terminal is mainly related to the number of container movements and the weather of each day. With the introduction of electric mobility for heavy duty container carriers at a seaport container terminal short-term load forecasting gains an important part in the procurement process. This paper describes a case-based approach to the forecasting of the electricity c...

Forecasting systems usually use historical data and specific methods (typically statistical models) to derive decisional information. To be accurate, the volume of historical data is often very large and the calculation and the exploration processes are complex. Anticipeo is one of the forecasting systems which is devoted to the prediction of sales based on collected sales over a “long” period ...

Identifying the most appropriate time series model to achieve a good forecasting accuracy is a challenging task. We propose a novel algorithm that aims to mitigate the importance of model selection, while increasing the accuracy. Multiple time series are constructed from the original time series, using temporal aggregation. These derivative series highlight different aspects of the original dat...

This paper describes an ongoing project to develop a demand forecasting model for clean-fuel vehicles in California. Large-scale surveys of both households and commercial fleet operators have been carried out. These data are being used to calibrate a new micro-simulation based vehicle demand forecasting system. Based on pre-specified attributes of future vehicles (including specified clean-fuel...

A time series is a sequence of observations of a random variable. Hence, it is a stochastic process. Forecasting time series data is important component of operations research because these data often provide the foundation for decision models. This models are used to predict data points before they are measured based on known past events. Researches in this subject have been done in many areas...

STUDY OBJECTIVE The aim was to evaluate the current approach to forecasting hospital bed requirements. DESIGN The study was a time series and regression analysis. The time series for mean duration of stay for general surgery in the age group 15-44 years (1969-1982) was used in the evaluation of different methods of forecasting future values of mean duration of stay and its subsequent use in t...

Emerging problems of environmental change and of long range hydrologic forecasting demand knowledge of the hydrologic cycle at global rather than catchment scale. Changes in atmosphere and/or landscape characteristics modify the earth's metabolism through changes in its biogeochemical cycles. The most basic of these is the water cycle which directly affects the global circulation of both atmosp...

In this paper a fuzzy and cointegration analysis are used to forecast tourist demand in Greece. In the first part, Johansen’s maximum likelihood techniques are applied to estimate the long-run relationships for the principal five tourist generating countries in Greece. In the second part, the estimated error correction terms are introduced to the first difference models to estimate the five sho...