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- Time series forecasting of styrene price using a hybrid ARIMA and neural network model
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- A New Hybrid Methodology for Nonlinear Time Series Forecasting
Time series forecasting of styrene price using a hybrid ARIMA and neural network model
Every player in the market has a greater need to know about the smallest change in the market. Therefore, the ability to see what is ahead is a valuable advantage. The purpose of this research is to make an attempt to understand the behavioral patterns and try to find a new hybrid forecasting approach based on ARIMA-ANN for estimating styrene price. The time series analysis and forecasting is an essential tool which could be widely useful for finding the significant characteristics for making future decisions.
Experimental results with real data sets show that the combined model can be most suitable to improve forecasting accurateness rather than traditional time series forecasting methodologies. As a subset of the literature, the small number of studies have been done to realize the new forecasting methods for forecasting styrene price. Proposal of Policy for Free Access Periodics.
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Drought is a water shortage that is caused by an imbalance between supply and demand. As one of the most severe natural disasters, drought exerts relatively widespread effects on human society that usually last for several months or even a few years, causing huge economic loss, reductions in food yield, starvation, and land degradation Piao et al. China is located in the East Asian monsoon region, with complex geographical conditions, complex climate changes, and frequent climate disasters. As climate warming and drying become increasingly apparent, the occurrence of natural disasters has increased significantly. Affected by specific climatic conditions, topographical features, and water resources, China is one of the countries with the most frequent and severe drought in the world.
Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. DOI: Zhang Published Computer Science Neurocomputing. Abstract Autoregressive integrated moving average ARIMA is one of the popular linear models in time series forecasting during the past three decades. Recent research activities in forecasting with artificial neural networks ANNs suggest that ANNs can be a promising alternative to the traditional linear methods. View via Publisher.
Every player in the market has a greater need to know about the smallest change in the market. Therefore, the ability to see what is ahead is a valuable advantage. The purpose of this research is to make an attempt to understand the behavioral patterns and try to find a new hybrid forecasting approach based on ARIMA-ANN for estimating styrene price. The time series analysis and forecasting is an essential tool which could be widely useful for finding the significant characteristics for making future decisions. Experimental results with real data sets show that the combined model can be most suitable to improve forecasting accurateness rather than traditional time series forecasting methodologies.
A New Hybrid Methodology for Nonlinear Time Series Forecasting
Authors: Fengxia Zheng , Shouming Zhong. ANNARIMA that combines both autoregressive integrated moving average ARIMA model and artificial neural network ANN model is a valuable tool for modeling and forecasting nonlinear time series, yet the over-fitting problem is more likely to occur in neural network models. This method is examined by using the data of Canadian Lynx data. Commenced in January
Skip to Main Content. A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. Use of this web site signifies your agreement to the terms and conditions. A hybrid forecasting approach using ARIMA models and self-organising fuzzy neural networks for capital markets Abstract: Linear time series models, such as the autoregressive integrated moving average ARIMA model, are among the most popular statistical models used to forecast time series. In recent years non-linear computational models, such as artificial neural networks ANN , have been shown to outperform traditional linear models when dealing with complex data, like financial time series.
Artificial neural networks ANNs are flexible computing frameworks and universal approximators that can be applied to a wide range of forecasting problems with a high degree of accuracy. However, using ANNs to model linear problems have yielded mixed results, and hence; it is not wise to apply them blindly to any type of data. This is the reason that hybrid methodologies combining linear models such as ARIMA and nonlinear models such as ANNs have been proposed in the literature of time series forecasting. Despite of all advantages of the traditional methodologies for combining ARIMA and ANNs, they have some assumptions that will degenerate their performance if the opposite situation occurs. In this paper, a new methodology is proposed in order to combine the ANNs with ARIMA in order to overcome the limitations of traditional hybrid methodologies and yield more general and more accurate hybrid models.
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