Vague and incomplete data represented as linguistic values massively exists in diverse real-word applications. The task of forecasting fuzzy time series under uncertain circumstances is thus of great important but difficult. The inherent uncertainty involving time evolution usually makes the transition of states in a system probabilistic. In this paper, we proposed a new forecasting model based...

In today’s world, using quantitative methods are very important for financial markets forecast, improvement of decisions and investments. In recent years, various time series forecasting methods have been proposed for financial markets forecasting. In each case, the accuracy of time series methods fundamental to make decision and hence the research for improving the effectiveness of forecasting...

We review the past 25 years of time series research that has been published in journals managed by the International Institute of Forecasters (Journal of Forecasting 1982–1985; International Journal of Forecasting 1985–2005). During this period, over one third of all papers published in these journals concerned time series forecasting. We also review highly influential works on time series fore...

Summery Various forecasting methods have been developed on the basis of fuzzy time series data, but accuracy has been matter of concern in these forecasts. As in fuzzy time series methods forecasted values depend to some degree on our interpretation of the output of the forecasting model thus different interpretation may lead to different results, this makes the process quite subjective. An obj...

The increasing availability of large amounts of historical data and the need of performing accurate forecasting of future behavior in several scientific and applied domains demands the definition of robust and efficient techniques able to infer from observations the stochastic dependency between past and future. The forecasting domain has been influenced, from the 1960s on, by linear statistica...

We are investigating techniques for producing textual summaries of time series data. Deep reasoning techniques have proven impractical because we lack perfect knowledge about users and their tasks. Data analysis techniques such as segmentation are more attractive, but they have been developed for data mining, not for communication. We examine how segmentation should be modified to make it suita...

The aim of this contribution is to show the opportunities of applying of fuzzy time series models to predict multiple heterogeneous time series, given at International Time Series Forecasting Competition [http://irafm.osu.cz/cif/main.php]. The dataset of this competition includes 91 time series of different length, time frequencies and behaviour. In this paper the framework (algorithm) of multi...

Three new hybrid neural models which are based upon the basic neural model put forth by McCulloch and Pitts (Haykin, 1999) and the compensatory neural models by Sinha et al. (2000), (2001) are proposed in this paper. The basic neural and the compensatory neural models are modified to take into account any linear dependence of the outputs on the inputs. This makes the hybrid models suitable for ...

This paper is concerned with time series forecasting in the presence of a large number of predictors. The results are of interest, for instance, in macroeconomic and financial forecasting where often many potential predictor variables are available. Most of the current forecast methods with many predictors consist of two steps, where the large set of predictors is first summarized by means of a...

This article links parameterized model mortality schedules with time series methods to develop forecasts of U.S. mortality to the year 2000. The use of model mortality schedules permits a relatively concise representation of the history of mortality by age and sex from 1900 to 1985, and the use of modern time series methods to extend this history forward to the end of this century allows for a ...