An artificial neural network (p,d,q) model for timeseries forecasting
01 Jan 2010-Expert Systems With Applications (EXPERT SYSTEMS WITH APPLICATIONS)-Vol. 37, Iss: 1, pp 479-489
TL;DR: The empirical results with three well-known real data sets indicate that the proposed model can be an effective way to improve forecasting accuracy achieved by artificial neural networks, and can be used as an appropriate alternative model for forecasting task, especially when higher forecasting accuracy is needed.
Abstract: Artificial neural networks (ANNs) are flexible computing frameworks and universal approximators that can be applied to a wide range of time series forecasting problems with a high degree of accuracy. However, despite all advantages cited for artificial neural networks, their performance for some real time series is not satisfactory. Improving forecasting especially time series forecasting accuracy is an important yet often difficult task facing forecasters. Both theoretical and empirical findings have indicated that integration of different models can be an effective way of improving upon their predictive performance, especially when the models in the ensemble are quite different. In this paper, a novel hybrid model of artificial neural networks is proposed using auto-regressive integrated moving average (ARIMA) models in order to yield a more accurate forecasting model than artificial neural networks. The empirical results with three well-known real data sets indicate that the proposed model can be an effective way to improve forecasting accuracy achieved by artificial neural networks. Therefore, it can be used as an appropriate alternative model for forecasting task, especially when higher forecasting accuracy is needed.
Citations
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TL;DR: The effectiveness of neural network models which are known to be dynamic and effective in stock-market predictions are evaluated, including multi-layer perceptron (MLP), dynamic artificial neural network (DAN2) and the hybrid neural networks which use generalized autoregressive conditional heteroscedasticity (GARCH) to extract new input variables.
Abstract: Forecasting stock exchange rates is an important financial problem that is receiving increasing attention. During the last few years, a number of neural network models and hybrid models have been proposed for obtaining accurate prediction results, in an attempt to outperform the traditional linear and nonlinear approaches. This paper evaluates the effectiveness of neural network models which are known to be dynamic and effective in stock-market predictions. The models analysed are multi-layer perceptron (MLP), dynamic artificial neural network (DAN2) and the hybrid neural networks which use generalized autoregressive conditional heteroscedasticity (GARCH) to extract new input variables. The comparison for each model is done in two view points: Mean Square Error (MSE) and Mean Absolute Deviate (MAD) using real exchange daily rate values of NASDAQ Stock Exchange index.
641 citations
Cites background from "An artificial neural network (p,d,q..."
...Khashei and Bijari (2010) compaired autoregressive integrated moving average (ARIMA), artificial neural networks (ANNs), and Zhang’s hybrid model....
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TL;DR: This paper examines the forecasting performance of ARIMA and artificial neural networks model with published stock data obtained from New York Stock Exchange to reveal the superiority of Neural networks model over ARimA model.
Abstract: This paper examines the forecasting performance of ARIMA and artificial neural networks model with published stock data obtained from New York Stock Exchange. The empirical results obtained reveal the superiority of neural networks model over ARIMA model. The findings further resolve and clarify contradictory opinions reported in literature over the superiority of neural networks and ARIMA model and vice versa.
381 citations
Cites background from "An artificial neural network (p,d,q..."
...As stated in [4], ANNs are data-driven, self-adaptive methods with few prior assumptions....
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...Finally, ANNs have been found to be very efficient in solving nonlinear problems including those in real world [4]....
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TL;DR: A novel hybrid model is proposed for prediction of stocks returns which is hybrid of two linear models and a non-linear model which outperforms recurrent neural network.
Abstract: A novel hybrid model is proposed for prediction of stocks returns.The proposed model is hybrid of two linear models and a non-linear model.An optimization model is introduced which generates weights for proposed model.Proposed model is able to capture non-linear patterns of stock data very well. In this paper, we propose a robust and novel hybrid model for prediction of stock returns. The proposed model is constituted of two linear models: autoregressive moving average model, exponential smoothing model and a non-linear model: recurrent neural network. Training data for recurrent neural network is generated by a new regression model. Recurrent neural network produces satisfactory predictions as compared to linear models. With the goal to further improve the accuracy of predictions, the proposed hybrid prediction model merges predictions obtained from these three prediction based models. An optimization model is introduced which generates optimal weights for proposed model; the model is solved using genetic algorithms. The results confirm about the accuracy of the prediction performance of recurrent neural network. As expected, an outstanding prediction performance has been obtained from proposed hybrid prediction model as it outperforms recurrent neural network. The proposed model is certainly expected to be a promising approach in the field of prediction based models where data is non-linear, whose patterns are difficult to be captured by traditional models.
364 citations
Journal Article•
TL;DR: In this paper, a new time-series forecasting model based on the flexible neural tree (FNT) is introduced. But the model is not suitable for time series forecasting and it is difficult to select the proper input variables or time-lags for constructing a time series model.
Abstract: Time-series forecasting is an important research and application area. Much effort has been devoted over the past several decades to develop and improve the time-series forecasting models. This paper introduces a new time-series forecasting model based on the flexible neural tree (FNT). The FNT model is generated initially as a flexible multi-layer feed-forward neural network and evolved using an evolutionary procedure. Very often it is a difficult task to select the proper input variables or time-lags for constructing a time-series model. Our research demonstrates that the FNT model is capable of handing the task automatically. The performance and effectiveness of the proposed method are evaluated using time series prediction problems and compared with those of related methods.
272 citations
TL;DR: Although there are many studies that presented the application of neural network models, but few of them proposed new neural networks models for forecasting that considered theoretical support and a systematic procedure in the construction of model, this leads to the importance of formulating new models of neural networks.
270 citations
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TL;DR: Das Buch behandelt die Systemidentifizierung in dem theoretischen Bereich, der direkte Auswirkungen auf Verstaendnis and praktische Anwendung der verschiedenen Verfahren zur IdentifIZierung hat.
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"An artificial neural network (p,d,q..." refers methods in this paper
...Some other order selection methods have been proposed based on validity criteria, the information-theoretic approaches such as the Akaike’s information criterion (AIC) (Shibata, 1976) and the minimum description length (MDL) (Hurvich & Tsai, 1989; Jones, 1975; Ljung, 1987)....
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TL;DR: In this article, a complete revision of a classic, seminal, and authoritative book that has been the model for most books on the topic written since 1970 is presented, focusing on practical techniques throughout, rather than a rigorous mathematical treatment of the subject.
Abstract: From the Publisher:
This is a complete revision of a classic, seminal, and authoritative book that has been the model for most books on the topic written since 1970. It focuses on practical techniques throughout, rather than a rigorous mathematical treatment of the subject. It explores the building of stochastic (statistical) models for time series and their use in important areas of application forecasting, model specification, estimation, and checking, transfer function modeling of dynamic relationships, modeling the effects of intervention events, and process control. Features sections on: recently developed methods for model specification, such as canonical correlation analysis and the use of model selection criteria; results on testing for unit root nonstationarity in ARIMA processes; the state space representation of ARMA models and its use for likelihood estimation and forecasting; score test for model checking; and deterministic components and structural components in time series models and their estimation based on regression-time series model methods.
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10,118 citations
"An artificial neural network (p,d,q..." refers methods in this paper
...The Box and Jenkins (1976) methodology includes three iterative steps of model identification, parameter estimation, and diagnostic checking....
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...The Box and Jenkins (1976) methodology includes three iterative steps of model identification, parameter estimation, and diagnostic checking. The basic idea of model identification is that if a time series is generated from an ARIMA process, it should have some theoretical autocorrelation properties. By matching the empirical autocorrelation patterns with the theoretical ones, it is often possible to identify one or several potential models for the given time series. Box and Jenkins (1976) proposed to use the autocorrelation function (ACF) and the partial autocorrelation function (PACF) of the sample data as the basic tools to identify the order of the ARIMA model....
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...In our proposed model, based on Box and Jenkins (1976) methodology in linear modeling, a time series is considered as nonlinear function of several past observations and random errors as follows:...
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...In our proposed model, based on Box and Jenkins (1976) methodology in linear modeling, a time series is considered as nonlinear function of several past observations and random errors as follows: yt ¼ f ½ðzt 1; zt 2; . . . ; zt mÞ; ðet 1; et 2; . . . ; et nÞ ; ð9Þ where f is a nonlinear function…...
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...Box and Jenkins (1976) proposed to use the autocorrelation function (ACF) and the partial autocorrelation function (PACF) of the sample data as the basic tools to identify the order of the ARIMA model....
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6,184 citations
TL;DR: In this article, a bias correction to the Akaike information criterion, called AICC, is derived for regression and autoregressive time series models, which is of particular use when the sample size is small, or when the number of fitted parameters is a moderate to large fraction of the sample sample size.
Abstract: SUMMARY A bias correction to the Akaike information criterion, AIC, is derived for regression and autoregressive time series models. The correction is of particular use when the sample size is small, or when the number of fitted parameters is a moderate to large fraction of the sample size. The corrected method, called AICC, is asymptotically efficient if the true model is infinite dimensional. Furthermore, when the true model is of finite dimension, AICC is found to provide better model order choices than any other asymptotically efficient method. Applications to nonstationary autoregressive and mixed autoregressive moving average time series models are also discussed.
5,867 citations
"An artificial neural network (p,d,q..." refers methods in this paper
...Some other order selection methods have been proposed based on validity criteria, the information-theoretic approaches such as the Akaike’s information criterion (AIC) (Shibata, 1976) and the minimum description length (MDL) (Hurvich & Tsai, 1989; Jones, 1975; Ljung, 1987)....
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