Time Series Forecasting Using A Hybrid Arima And Neural Network Model Pdf

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A New Hybrid Methodology for Nonlinear Time Series Forecasting

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.

Linear forecasting models have played major roles in many applications for over a century. If error terms in models are normally distributed, linear models are capable of producing the most accurate forecasting results. The central limit theorem CLT provides theoretical support in applying linear models. During the last two decades, nonlinear models such as neural network models have gradually emerged as alternatives in modeling and forecasting real processes. In hydrology, neural networks have been applied to rainfall-runoff estimation as well as stream and peak flow forecasting. Successful nonlinear methods rely on the generalized central limit theorem GCLT , which provides theoretical justifications in applying nonlinear methods to real processes in impulsive environments.

Time series forecasting of styrene price using a hybrid ARIMA and neural network model

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. This paper proposes a novel hybrid forecasting model which exploits the linear modelling strengths of the ARIMA model, and the flexibility of a self-organising fuzzy neural network SOFNN.

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. Empirical results with Canadian Lynx data set indicate that the proposed methodology can be a more effective way in order to combine linear and nonlinear models together than traditional hybrid methodologies.

Time series forecasting of styrene price using a hybrid ARIMA and neural network model

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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.

This paper proposes a novel hybrid forecasting model combining autoregressive integrated moving average ARIMA and artificial neural network ANN with incorporating moving average and the annual seasonal index for Thailand's cassava export i. The comprehensive experiments are conducted to investigate the appropriate parameters of the proposed model as well as other forecasting models compared. Therefore, the proposed model can be used as an alternative forecasting method for stakeholders making a decision in cassava international trading to obtain better accuracy in predicting future export of native starch and modified starch which are the majority of the total export. Time series forecasting is an important research area which has attracted a lot of attention from research communities in numerous practical fields including statistics, business, econometrics, finance, weather forecasting, earthquake prediction, etc.

Dissertation/Thesis Abstract

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.

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ГЛАВА 55 - Ты уселся на мое место, осел. Беккер с трудом приподнял голову. Неужели в этой Богом проклятой стране кто-то говорит по-английски.

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3 Response
  1. Gary B.

    Experimental results with real data sets indicate that the combined model can be an effective way to improve forecasting accuracy achieved by either of the models.

  2. Hilaire P.

    Request PDF | Zhang, G.P.: Time Series Forecasting Using a Hybrid ARIMA and Neural Network Model. Neurocomputing 50,

  3. Jessie123Xo

    Then, the Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Network (ANN) models are used to separately recognize and.

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