The existing techniques of forecasting a future count either treat the time series of counts as a Gaussian time series or use a random effects based dynamic Poisson model. The normality based approach may not yield valid forecasting, whereas the random effects based model usually generates a complex correlation structure for the time series of counts which may be impractical to use for forecast...

A preliminary version, please do not quote

this paper presents the prediction of vehicle's velocity time series using neural networks. for this purpose, driving data is firstly collected in real world traffic conditions in the city of tehran using advance vehicle location devices installed on private cars. a multi-layer perceptron network is then designed for driving time series forecasting. in addition, the results of this study a...

Demand Forecasting is an essential process for any firm whether it is a supplier, manufacturer or retailer. A large number of research works about time series forecast techniques exists in the literature, and there are many time series forecasting tools. In many cases, however, selecting the best time series forecasting model for each time series to be dealt with is still a complex problem. In ...

The point-valued time series (PTS) is simply about one value in each or period of the data, but when data have two values at time, suitable called interval-valued (ITS). An example ITS daily close and open stock prices. If are typed linguistic for example, “low increase”, “medium increase” “high a fuzzy-valued being well-known as fuzzy (FTS). aim this study to compare PTS FTS models forecasting...

The developed software is a web application with open access and is aimed on forecasting of time series stored in database. We proposed approach of time series forecasting, combined ARIMA models with fuzzy techniques: three fuzzy time series models, fuzzy transformation (F-transform) and ACL-scale. Applications of a proposed web service have demonstrated efficiency in practical time series pred...

Both theoretical and empirical findings have suggested that combining different models can be an effective way to improve the predictive performance of each individual model. It is especially occurred when the models in the ensemble are quite different. Hybrid techniques that decompose a time series into its linear and nonlinear components are one of the most important kinds of the hybrid model...

The fuzzy time series (FTS) forecasting models have been being studied intensively over the past few years. Most of researches focus on improving effectiveness FTS using time-invariant logical relationship groups proposed by Chen et al. In contrast to Chen’s model, a set can be repeated in right-hand side Yu’s model. N. C. Dieu enhanced model time-variant instead ones. mentioned above partition...