Nonlinear Autoregressive Neural Network and Extended Kalman Filters for Prediction of Financial Time Series

Benrhmach, Ghassane; Namir, Khalil; Namir, Abdelwahed; Bouyaghroumni, Jamal

doi:10.1155/2020/5057801

Cited by 55 publications

(22 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The training of the NN is pointed toward approximating the function by methods for optimizing the network weights and neuron bias. Along these lines, a NAR model is characterized decisively by a condition of the sort (5) where b is the number of entries, j is the number of hidden layers with activation function , and is the parameter corresponding to the weight of the connection among the input layer a and the hidden layer k, is the weight of the connection among the hidden layer k and the output unit, and are the constants that correspond, respectively, to the hidden layer k and the output unit [ 36 ]. Figure 3 indicates the 13 inputs with 10 hidden layers in the NAR-NN model.…”

Section: Methodsmentioning

confidence: 99%

Self-organizing Maps and Bayesian Regularized Neural Network for Analyzing Gasoline and Diesel Price Drifts

Sujatha

Chatterjee

Priyadarshini

et al. 2022

Int J Comput Intell Syst

View full text Add to dashboard Cite

Any nation’s growth depends on the trend of the price of fuel. The fuel price drifts have both direct and indirect impacts on a nation’s economy. Nation’s growth will be hampered due to the higher level of inflation prevailing in the oil industry. This paper proposed a method of analyzing Gasoline and Diesel Price Drifts based on Self-organizing Maps and Bayesian regularized neural networks. The US gasoline and diesel price timeline dataset is used to validate the proposed approach. In the dataset, all grades, regular, medium, and premium with conventional, reformulated, all formulation of gasoline combinations, and diesel pricing per gallon weekly from 1995 to January 2021, are considered. For the data visualization purpose, we have used self-organizing maps and analyzed them with a neural network algorithm. The nonlinear autoregressive neural network is adopted because of the time series dataset. Three training algorithms are adopted to train the neural networks: Levenberg-Marquard, scaled conjugate gradient, and Bayesian regularization. The results are hopeful and reveal the robustness of the proposed model. In the proposed approach, we have found Levenberg-Marquard error falls from − 0.1074 to 0.1424, scaled conjugate gradient error falls from − 0.1476 to 0.1618, and similarly, Bayesian regularization error falls in − 0.09854 to 0.09871, which showed that out of the three approaches considered, the Bayesian regularization gives better results.

show abstract

Section: Methodsmentioning

confidence: 99%

Self-organizing Maps and Bayesian Regularized Neural Network for Analyzing Gasoline and Diesel Price Drifts

Sujatha

Chatterjee

Priyadarshini

et al. 2022

Int J Comput Intell Syst

View full text Add to dashboard Cite

show abstract

“…Nonlinear Autoregressive Neural Network (NARNN) [40][41][42] . In recent years, the NARNN has achieved good results in time series analysis because of its good nonlinear characteristics, parallel distributed storage structure and high fault tolerance.…”

Section: Sarima Modelmentioning

confidence: 99%

Predictive analysis of the number of human brucellosis cases in Xinjiang, China

Zheng

Zhang

Wang

et al. 2021

Sci Rep

View full text Add to dashboard Cite

Brucellosis is one of the major public health problems in China, and human brucellosis represents a serious public health concern in Xinjiang and requires a prediction analysis to help making early planning and putting forward science preventive and control countermeasures. According to the characteristics of the time series of monthly reported cases of human brucellosis in Xinjiang from January 2008 to June 2020, we used seasonal autoregressive integrated moving average (SARIMA) method and nonlinear autoregressive regression neural network (NARNN) method, which are widely prevalent and have high prediction accuracy, to construct prediction models and make prediction analysis. Finally, we established the SARIMA((1,4,5,7),0,0)(0,1,2)12 model and the NARNN model with a time lag of 5 and a hidden layer neuron of 10. Both models have high fitting performance. After comparing the accuracies of two established models, we found that the SARIMA((1,4,5,7),0,0)(0,1,2)12 model was better than the NARNN model. We used the SARIMA((1,4,5,7),0,0)(0,1,2)12 model to predict the number of monthly reported cases of human brucellosis in Xinjiang from July 2020 to December 2021, and the results showed that the fluctuation of the time series from July 2020 to December 2021 was similar to that of the last year and a half while maintaining the current prevention and control ability. The methodology applied here and its prediction values of this study could be useful to give a scientific reference for prevention and control human brucellosis.

show abstract

“…With the time series data, lagged values of the time series can be used as inputs to a neural network, so-called this the NARNN model. Mathematically, the NARNN model [2] can be written by the equation of the form as:…”

Section: Nonlinear Autoregressive Neural Network (Narnn) Modelmentioning

confidence: 99%

Modeling and Forecasting Long-Term Records of Mean Sea Level at Grand Isle, Louisiana: SARIMA, NARNN, and Mixed SARIMA-NARNN Models

Nain

2021

J. Appl. Data. Sciences.

View full text Add to dashboard Cite

This study tried to demonstrate the role of time series models in modeling and forecasting process using long-term records of monthly mean sea level from January 1978 to October 2020 at Grand Isle, Louisiana. Following the Box-Jenkins methodology, the ARIMA(1,1,1)(2,0,0) 12 with drift model was selected to be the best fit model for the time series, according to its lowest AIC value. Using the LM algorithm, the results revealed that the NARNN model with 9 neurons in the hidden layer and 6 time delays provided the best performance in the nonlinear autoregressive neural network models at its smaller MSE value. The Mixed model, a combination of the SARIMA and NARNN models has both linear and nonlinear modelling capabilities can be a better choice for modelling the time series. The comparative results revealed that the Mixed-LM model with 9 neurons in the hidden layer and 3 time delays yielded higher accuracy than the NARNN-LM model with 9 neurons in the hidden layer and 6 time delays, and the ARIMA(1,1,1)(2,0,0) 12 with drift model, according to its lowest MSE in this study. Thus, this study may provide an integrated modelling approach as a decision-making supportive method for formulating local mean sea level forecast in advance. Understanding past sea level is important for the analysis of current and future sea level changes. In order to sustain these observations, research programs utilizing the resulting data should be able to improve significantly our understanding and narrow projections of future sea level rise and variability.

show abstract

Nonlinear Autoregressive Neural Network and Extended Kalman Filters for Prediction of Financial Time Series

Cited by 55 publications

References 9 publications

Self-organizing Maps and Bayesian Regularized Neural Network for Analyzing Gasoline and Diesel Price Drifts

Self-organizing Maps and Bayesian Regularized Neural Network for Analyzing Gasoline and Diesel Price Drifts

Predictive analysis of the number of human brucellosis cases in Xinjiang, China

Modeling and Forecasting Long-Term Records of Mean Sea Level at Grand Isle, Louisiana: SARIMA, NARNN, and Mixed SARIMA-NARNN Models

Contact Info

Product

Resources

About