Time series forecasting using a hybrid ARIMA and neural network model

Zhang, G.Peter

doi:10.1016/s0925-2312(01)00702-0

Cited by 3,264 publications

(2,041 citation statements)

References 36 publications

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“…This confirms that simple network structure that has a small number of hidden nodes often works well in out-of-sample forecasting [16]- [20]. This can be due to the over fitting problem in neural network modeling process that allows the established network to fit the training data well, but poor generalization may happen.…”

Section: Journal Of Economics Business and Management Vol 3 Nosupporting

confidence: 61%

Modeling Gold Price via Artificial Neural Network

Mombeini¹,

Yazdani–Chamzini²

2015

JOEBM

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Section: Journal Of Economics Business and Management Vol 3 Nosupporting

confidence: 61%

Modeling Gold Price via Artificial Neural Network

Mombeini¹,

Yazdani–Chamzini²

2015

JOEBM

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“…On the other hand, the neural network approach to forecasting [13] allows us to approximate almost any non-linear dependence, hidden in input data. In addition, neural networks as forecasting models established themselves as sufficiently flexible and fast tools for an analysis of historical data [11].…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Development of specialized services for predicting the business activity indicators based on micro–service architecture

Oksanych

Shevchenko

Shcherbak

et al. 2017

EEJET

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“…In effect, since observations from a time series ( ) that exhibit dependency on past values may be seen as points of the domain of an unknown compact support function, it follows that the ANNs are capable of approximating them (for modeling or forecasting) with a high degree of accuracy. According to [22], the predictive power of ANNs comes from the parallel processing of the information exhibited by the data. In addition, AAN models are largely determined by the stochastic characteristics inherent in the time series.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Time series forecasting with the WARIMAX-GARCH method

et al. 2016

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Time series forecasting with the WARIMAX-GARCH method, Neurocomputing, http://dx.doi.org/10. 1016/j.neucom.2016.08.046 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. AbstractIt is well-known that causal forecasting methods that include appropriately chosen Exogenous Variables (EVs) very often present improved forecasting performances over univariate methods. However, in practice, EVs are usually difficult to obtain and in many cases are not available at all. In this paper, a new causal forecasting approach, called Wavelet Auto-Regressive Integrated Moving Average with eXogenous variables and Generalized Auto-Regressive Conditional Heteroscedasticity (WARIMAX-GARCH) method, is proposed to improve predictive performance and accuracy but also to address, at least in part, the problem of unavailable EVs. Basically, the WARIMAX-GARCH method obtains Wavelet "EVs" (WEVs) from Auto-Regressive Integrated Moving Average with eXogenous variables and Generalized Auto-Regressive Conditional Heteroscedasticity (ARIMAX-GARCH) models applied to Wavelet Components (WCs) that are initially determined from the underlying time series. The WEVs are, in fact, treated by the WARIMAX-GARCH method as if they were conventional EVs. Similarly to GARCH and ARIMA-GARCH models, the WARIMAX-GARCH method is suitable for time series exhibiting non-linear characteristics such as conditional variance that depends on past values of observed data. However, unlike those, it can explicitly model frequency domain patterns in the series to help improve predictive performance. An application to a daily time series of dam displacement in Brazil shows the WARIMAX-GARCH method to remarkably outperform the ARIMA-GARCH method, as well as the (multi-layer perceptron) Artificial Neural Network (ANN) and its wavelet version referred to as Wavelet Artificial Neural Network (WANN) as in [1], on statistical measures for both in-sample and out-of-sample forecasting.

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Time series forecasting using a hybrid ARIMA and neural network model

Cited by 3,264 publications

References 36 publications

Modeling Gold Price via Artificial Neural Network

Modeling Gold Price via Artificial Neural Network

Development of specialized services for predicting the business activity indicators based on micro–service architecture

Time series forecasting with the WARIMAX-GARCH method

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