Predicting daily occupancy is extremely important for the revenue management of individual hotels. However, daily occupancy can fluctuate widely and is difficult to forecast accurately based on existing forecasting methods. In this paper, Ensemble Empirical Mode Decomposition (EEMD)—a novel method—is introduced, and an individual hotel is chosen to test the effectiveness of EEMD in combination ...

Problem statement: Load forecasting plays an important task in power system planning, operation and control. It has received an increasing attention over the years by academic researchers and practitioners. Control, security assessment, optimum planning of power production required a precise short term load forecasting. Approach: This study tries to combine neural network and fuzzy logic for ne...

Stata 10 is a powerful, versatile, and flexible statistical package with a wide range of userfriendly and accurate time series analytical and forecasting commands. It was released in June 2007, about two years after Stata 9 appeared. Prospective users want to know what features the package offers, while veteran users want to know what is novel about the latest version(s). This review addresses ...

Forecasting is an important issue for many different applications. In this paper, a new efficient forecasting technique is presented. Such technique is designed by using fast neural networks (FNNs). The new idea relies on performing cross correlation in the frequency domain between the input data and the input weights of neural networks. It is proved mathematically and practically that the numb...

Accurate power generation forecasting provides the basis of decision making for electric power industry development plans, energy conservation and environmental protection. Since the power generation time series are rarely purely linear or nonlinear, no single forecasting model can identify the true data trends exactly in all situations. To combine forecasts from different models can reduce the...

This study extends the existing forecasting accuracy debate in the tourism literature by examining the forecasting performance of various vector autoregressive (VAR) models. In particular, this study seeks to ascertain whether the introduction of the Bayesian restrictions (priors) to the unrestricted VAR process would lead to an improvement in forecasting performance in terms of achieving a hig...

Accurate forecasting of icing thickness has a great significance for ensuring the security and stability of power grid. In order to improve the forecasting accuracy, this paper proposes an icing forecasting system based on fireworks algorithm and weighted least square support vector machine (W-LSSVM). The method of fireworks algorithm is employed to select the proper input features with the pur...

Many forecasting models based on the concepts of Fuzzy time series have been proposed in the past decades. These models have been widely applied to various problem domains, especially in dealing with forecasting problems in which historical data are linguistic values. In this paper, we present a new fuzzy time series forecasting model, which uses the historical data as the universe of discourse...