The goal of this work is to derive models for forecasting the final price of ongoing online auctions. This forecasting task is important not only to the participants of an auction who compete against each other for the lowest price, but also to designers of bidder-side agents. Forecasting prices in online auctions is challenging from a statistical pointof-view because traditional forecasting mo...

Precipitation forecasting due to its random nature in space and time always faced with many problems and this uncertainty reduces the validity of the forecasting model. Nowadays nonlinear networks as intelligent systems to predict such complex phenomena are widely used. One of the methods that have been considered in recent years in the fields of hydrology is use of wavelet transform as a moder...

Flood forecasting is always a challenge in Taiwan, which has a subtropical climate and high mountains. This paper develops a fuzzy clustering neural network (FCNN), and implements this novel structure and reasoning process for flood forecasting. The FCNN has a hybrid learning scheme; the unsupervised learning scheme employs fuzzy min-max clustering to extract information from the input data. Th...

The analysis proceeds with a review of the problem of forecasting transportation requirements. Several idiosyncratic models are examined and their properties specified. The forecasting of transportation stocks and commodity flows is discussed. Models of the demand for transportation services are implemented with empirical data. Graph theory as an interpretative frame of reference is introduced ...

Many models have been developed to forecast wind farm power output. It is generally difficult to determine whether the performance of one model is consistently better than that of another model under all circumstances. Motivated by this finding, we aimed to integrate groups of models into an aggregated model using fuzzy theory to obtain further performance improvements. First, three groups of l...

Although artificial neural networks (ANN) promise superior performance in forecasting theory, they are not yet an established method in business practice. The vast degrees of freedom to parameterize ANNs have lead to countless heuristic approaches to simplify modeling, training, network selection and evaluation implemented with varying success. Consequently, a systematic evaluation is required ...

This paper surveys recent literature in the area of stock market forecasting using advanced engineering based methods like Neural Network, fractal theory, Data Mining, Hidden Markov Model and Neuro-Fuzzy system. Neural Networks and Neuro-Fuzzy systems are emerging as an effective tool to be used in the forecasting of stock market especially in machine learning techniques. Due to chaotic behavio...

-This paper introduced a method of decomposing non-stationary runoff time series. By wavelet decomposing, the runoff time series is decomposed into stationary time series and stochastic time series, and AR(n) model be imposed for forecasting stationary time series. By studying chaos characteristic of stochastic time series, this paper put forward a nonlinear chaos dynamics-forecasting model to ...

Accurate forecasting of fossil fuel energy consumption for power generation is important and fundamental for rational power energy planning in the electricity industry. The least squares support vector machine (LSSVM) is a powerful methodology for solving nonlinear forecasting issues with small samples. The key point is how to determine the appropriate parameters which have great effect on the ...

analyses and investigations on river flow behavior are major issues in design, operation and studies related to water engineering. thus, recently the application of chaos theory and new techniques, such as chaos theory, has been considered in hydrology and water resources due to relevant innovations and ability. this paper compares the performance of chaos theory with anfis model and discusses ...