Using extensive diary data from people taking their driver's license exam, the authors investigated the role of time in affective forecasting accuracy. Replicating existing findings, participants grossly overestimated the intensity and duration of their negative affect after failure and only slightly overestimated the intensity and duration of their positive affect after success. Extending exis...

In this paper, we proposed a hybrid algorithm to forecast enrolment based on fuzzy time series and genetic algorithms, the proposed algorithms presents a good forecasting result with higher accuracy rate. Historical enrolment of the University of Alabama from year 1948 to 2010 are used in this study to illustrate the forecasting process.

in recent years, various time series models have been proposed for financial markets forecasting. in each case, the accuracy of time series forecasting models are fundamental to make decision and hence the research for improving the effectiveness of forecasting models have been curried on. many researchers have compared different time series models together in order to determine more efficient ...

This study aims to analyze the effects of data pre-processing on the forecasting performance of neural network models. We use three different Artificial Neural Networks techniques to predict tourist demand: multi-layer perceptron, radial basis function and Elman neural networks. The structure of the networks is based on a multiple-output approach. We use official statistical data of inbound int...

In every field of life, people are interested to be able to forecast future. A number of techniques are available to predict and forecasting upto a certain level of accuracy. Many techniques involve statistical tools and techniques for forecasting, modeling and control. Use of statistical techniques is growing with time and new techniques are being developed very rapidly. Especially in the fiel...