Time series modelling and forecasting is a vibrant research field that had attracted the interest of scientific community in recent decades. Forecasts agricultural prices are proposed to be useful for farmers, governments, policy makers agribusiness industries. In this study, an effort made compare capabilities well-known linear Auto Regressive Integrated Moving Average (ARIMA) models, Delay Ne...

this study is aimed at comparison of two quantitative model arima and madm in predicting manpower costs. also qualitative issue and problems of manpower planning are transformed in to qualitative one’s so as optimum planning and forecasting be made. in this study, we first used the time- series models especially arima, to fit forecasting manpower costs in shiraz hafez tile mfg. plant and extrac...

BACKGROUNDS/OBJECTIVE Schistosomiasis is still a major public health problem in China, despite the fact that the government has implemented a series of strategies to prevent and control the spread of the parasitic disease. Advanced warning and reliable forecasting can help policymakers to adjust and implement strategies more effectively, which will lead to the control and elimination of schisto...

Data mining is the search for valuable information in large volumes of data. Finding patterns in time series databases is important to a variety of applications, including stock market trading and budget forecasting. This paper reports on an extension of neural network methods for planning and budgeting in the State of Utah. In particular, historical time series are analyzed using stacked gener...

This article presents a new connectionist architecture for stochastic univariate signal prediction. After a review of related statistical and connectionist models pointing out their advantages and limitations, we introduce the-NARMA model as the simplest non-linear extension of ARMA models. These models then provide the units of a MLP-like neural network: the-NARMA neural network. The associate...

an adaptive version of growing and pruning rbf neural network has been used to predict the system output and implement linear model-based predictive controller (lmpc) and non-linear model-based predictive controller (nmpc) strategies. a radial-basis neural network with growing and pruning capabilities is introduced to carry out on-line model identification.an unscented kalman filter (ukf) algor...

This manuscript deals with the similarity querying problems for cases where data loss exists. Limitations in traditional methodologies for querying incomplete data in database, data mining and information retrieval research has urged to shift into development of different new innovative models. This Investigation is done based on a model developed based on ARIMA constructional model to check th...

We forecast high resolution solar irradiance time series using an exponential smoothing state space (ESSS) model. To stationarize the irradiance data before applying linear time series models, we propose a novel Fourier trend model and compare the performance with other popular trend models using residual analysis and the Kwiatkowski-Phillips-Schmidt-Shin (KPSS) stationarity test. Using the opt...