Chaotic time series prediction with residual analysis method using hybrid Elman–NARX neural networks

Ardalani-Farsa, Muhammad; Zolfaghari, Saeed

doi:10.1016/j.neucom.2010.06.004

Cited by 221 publications

(83 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…[19] is better using less training data for the sunspot data set a similar prediction performance after selection can be achieved compared to the literature, even when more data points are considered for the validation error.…”

Section: Resultsmentioning

confidence: 55%

Evolutionary multi-objective generation of recurrent neural network ensembles for time series prediction

2014

View full text Add to dashboard Cite

Section: Resultsmentioning

confidence: 55%

Evolutionary multi-objective generation of recurrent neural network ensembles for time series prediction

2014

View full text Add to dashboard Cite

“…Largest Lyapunov Exponent (LLE) [3] is used to determine if KLCI data is chaotic or not, a negative LLE means the time series is not chaotic and a positive LLE shows the existence of chaos in the tested time series. The following equation is used to obtain the lyapunov exponent:…”

Section: Resultsmentioning

confidence: 99%

“…Elman and NARX network [3] have been hybridized for chaotic forecasting too, the hybrid model minimized the problem of vanishing gradients in recurrent networks, but did not consider the over fitting problem. In this paper, a novel hybrid model is proposed for financial forecasting by addressing the main issues of vanishing gradient [12] and over fitting [7] in recurrent neural networks.…”

Section: Related Workmentioning

confidence: 99%

Hybridization of Ensemble Kalman Filter and Non-linear Auto-regressive Neural Network for Financial Forecasting

Abdulkadir

Yong

Marimuthu

et al. 2014

Mining Intelligence and Knowledge Exploration

View full text Add to dashboard Cite

Abstract. Financial data is characterized as non-linear, chaotic in nature and volatile thus making the process of forecasting cumbersome. Therefore, a successful forecasting model must be able to capture longterm dependencies from the past chaotic data. In this study, a novel hybrid model, called UKF-NARX, consists of unscented kalman filter and non-linear auto-regressive network with exogenous input trained with bayesian regulation algorithm is modelled for chaotic financial forecasting. The proposed hybrid model is compared with commonly used Elman-NARX and static forecasting model employed by financial analysts. Experimental results on Bursa Malaysia KLCI data show that the proposed hybrid model outperforms the other two commonly used models.

show abstract

“…ANNs can learn from patterns and capture hidden functional relationships in a given data even if the functional relationships are not known or difficult to identify [33], [34]. Using the training methods, an ANN can be trained to identify the underlying correlation between the inputs and outputs, and finally to generate appropriate outputs.…”

Section: Introductionmentioning

confidence: 99%

Using a Single Dendritic Neuron to Forecast Tourist Arrivals to Japan

Chen

Sun

Gao

et al. 2017

IEICE Trans. Inf. & Syst.

View full text Add to dashboard Cite

SUMMARYWith the fast growth of the international tourism industry, it has been a challenge to forecast the tourism demand in the international tourism market. Traditional forecasting methods usually suffer from the prediction accuracy problem due to the high volatility, irregular movements and non-stationarity of the tourist time series. In this study, a novel single dendritic neuron model (SDNM) is proposed to perform the tourism demand forecasting. First, we use a phase space reconstruction to analyze the characteristics of the tourism and reconstruct the time series into proper phase space points. Then, the maximum Lyapunov exponent is employed to identify the chaotic properties of time series which is used to determine the limit of prediction. Finally, we use SDNM to make a short-term prediction. Experimental results of the forecasting of the monthly foreign tourist arrivals to Japan indicate that the proposed SDNM is more efficient and accurate than other neural networks including the multi-layered perceptron, the neuro-fuzzy inference system, the Elman network, and the single multiplicative neuron model.

show abstract

Chaotic time series prediction with residual analysis method using hybrid Elman–NARX neural networks

Cited by 221 publications

References 35 publications

Evolutionary multi-objective generation of recurrent neural network ensembles for time series prediction

Evolutionary multi-objective generation of recurrent neural network ensembles for time series prediction

Hybridization of Ensemble Kalman Filter and Non-linear Auto-regressive Neural Network for Financial Forecasting

Using a Single Dendritic Neuron to Forecast Tourist Arrivals to Japan

Contact Info

Product

Resources

About