Forecasting of time series that have seasonal and other variations remains an important problem for forecasters. This paper presents a neural network (NN) approach along with a fuzzy time series methods to forecasting sugar production in India. The agriculture production and productivity is one of the such processes, which is not governed by any deterministic process due to highly non linearity...

In this paper, we focus on application of fuzzy transform (F-transform) to analysis of time series under the assumption that the latter can be additively decomposed into trend-cycle, seasonal component and noise. We prove that when setting properly width of the basic functions, the inverse F-transform of the time series closely approximates its trend-cycle. This means that the F-transform almos...

There is a growing recognition in the literature on business cycles that production technologies may give rise to complicated interactions between seasonal and cyclical movements in economic time series, which can distort business cycle inference based on seasonally adjusted data. For the most part, however, the empirical research in this area has relied on standard univariate seasonal adjustme...

the late detection of the kick (the entrance of underground fluids into oil wells) leads to oil wellblowouts. it causes human life loss and imposes a great deal of expenses on the petroleum industry.this paper presents the application of adaptive neuro-fuzzy inference system designed for an earlierkick detection using measurable drilling parameters. in order to generate the initial fuzzy infere...

  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 effi...