temperature and precipitation are among important atmospheric parameters for watershed planning. assessment of temperature and precipitation trends is very important for future watershed planning. in this paper, trends of atmospheric parameters such as seasonal and annual temperature and precipitation were examined for the synoptic stations of bandar anzali, rasht, ramsar, babolsar and gorgan. ...

since the pioneering work of zadeh, fuzzy set theory has been applied to amyriad of areas. song and chissom introduced the concept of fuzzy time series andapplied some methods to the enrolments of the university of alabama. thereafter weapply fuzzy techniques for system identification and apply statistical techniques tomodelling system. an automatic methodology framework that combines fuzzytech...

in the management of river basins, prediction of river water quality is essential to maintain water quality within standard limits. this study performed a time-series analysis of the prediction of chlorine concentration and electrical conductivity time series data for the period of 1991-2005 from sefidrood river in northern iran. the seasonal prediction of chlorine and electrical conductivity t...

fuzzy time series have been developed during the last decade to improve the forecast accuracy. many algorithms have been applied in this approach of forecasting such as high order time invariant fuzzy time series. in this paper, we present a hybrid algorithm to deal with the forecasting problem based on time variant fuzzy time series and particle swarm optimization algorithm, as a highly effici...

We define a new seasonal forecasting method based on fuzzy transforms. We use the best interpolating polynomial for extracting the trend of the time series and generate the inverse fuzzy transform on each seasonal subset of the universe of discourse for predicting the value of an assigned output. In the first example, we use the daily weather dataset of the municipality of Naples (Italy) starti...

A new methodology for the analysis and forecasting of time series is proposed. It directly employs two soft computing techniques: the fuzzy transform and the perception-based logical deduction. Thanks to the use of both these methods, and to the innovative approach, consisting of the construction of several independent models, the methodology is successfully applicable to robust long-time predi...

1 Università degli Studi di Napoli Federico II, Dipartimento di Architettura, via Toledo 402, 80134 Napoli 5 (Italy); fdimarti,[email protected] 6 * Correspondence: [email protected]; Tel.: +39-081-253-8907; Fax: +39-081-253-8905 7 8 Abstract: We define a new seasonal forecasting method based on fuzzy transforms. We use the best 9 interpolating polynomial for extracting the trend of the time serie...

Count time series are widely encountered in practice. As with continuous valued data, many count have seasonal properties. This paper uses a recent advance stationary to develop general modeling paradigm. The model constructed here permits any marginal distribution for the and most flexible autocorrelations possible, including those negative dependence. Likelihood methods of inference explored....

Revenue forecasting is difficult but essential for companies that want to create high-quality revenue budgets, especially in an uncertain economic environment with changing government policies. Under these conditions, the subjective judgment of decision makers is a crucial factor in making accurate forecasts. This investigation develops a fuzzy least-squares support vector regression model with...

This chapter deals with seasonal time series in economics and it reviews models that can be used to forecast out-of-sample data. Some of the key properties of seasonal time series are reviewed, and various empirical examples are given for illustration. The potential limitations to seasonal adjustment are reviewed. The chapter further addresses a few basic models like the deterministic seasonali...