In time series analysis, fitting the Moving Average (MA) model is more complicated than Autoregressive (AR) models because the error terms are not observable. This means that iterative nonlinear fitting procedures need to be used in place of linear least squares. In this paper, Time-Varying Moving Average (TVMA) models are proposed for an autocovariance nonstationary time series. Through statis...

conventionally, regression and time series analyses have been employed in modeling water demand forecasts. in recent years, the relatively new technique of neural networks (nns) has been proposed as an efficient tool for modeling and forecasting. the objective of this study is to investigate the relatively new technique of gmdh – type neural networks for the use of forecasting long – term urban...

In this work, the time series modeling was used to predict the Tazareh coal mine risks. For this purpose, initially, a monthly analysis of the risk constituents including frequency index and incidence severity index was performed. Next, a monthly time series diagram related to each one of these indices was for a nine year period of time from 2005 to 2013. After extrusion of the trend, seasonali...

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 method can be applied in predicting the situation food price development data such as rice. The position of rice a staple has resulted this commodity being one indicators economic growth. importance suppressing prices so that they are stable done by forecasting Indonesia future. research used for is average based fuzzy Markov chain and novel algorithms series. Researchers will...