Nonlinear autoregressive exogenous model (NARX) in stock price index's prediction

Wibowo, Antoni; Pujianto, Harry; Saputro, Dewi Retno Sari

doi:10.1109/icitisee.2017.8285507

Cited by 6 publications

(10 citation statements)

References 5 publications

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“…For example, a method of predicting the future yield based on the yield from the previous time point (t-1) to the time t or the yield from the previous time point of previous time point (t-n) to the time t is used. However, not only a time series model but also exogenous variables, which is a non-linear model, might be used to predict the yield more accurately when considering the effects of various environmental factors of the cultivation data of the tomato yield [4][5][6][7][8][9][10][11]. Up to now, the statistical models considered only used past data to predict present or future emergent values.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, nonlinear regression models are frequently used to predict future data values for such time series data. Therefore, until now, these nonlinear autoregressive exogeneous (NARX) neural networks have been applied to the analysis of various time series data [4][5][6][7][8][9][10][11]. Some typical cases where these NARX network models are applied are given as follows.…”

Section: Introductionmentioning

confidence: 99%

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Forecasting of Tomato Yields Using Attention-Based LSTM Network and ARMA Model

Cho

Kim

et al. 2021

Electronics

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Nonlinear autoregressive exogenous (NARX), autoregressive integrated moving average (ARIMA) and multi-layer perceptron (MLP) networks have been widely used to predict the appearance value of future points for time series data. However, in recent years, new approaches to predict time series data based on various networks of deep learning have been proposed. In this paper, we tried to predict how various environmental factors with time series information affect the yields of tomatoes by combining a traditional statistical time series model and a deep learning model. In the first half of the proposed model, we used an encoding attention-based long short-term memory (LSTM) network to identify environmental variables that affect the time series data for tomatoes yields. In the second half of the proposed model, we used the ARMA model as a statistical time series analysis model to improve the difference between the actual yields and the predicted yields given by the attention-based LSTM network at the first half of the proposed model. Next, we predicted the yields of tomatoes in the future based on the measured values of environmental variables given during the observed period using a model built by integrating the two models. Finally, the proposed model was applied to determine which environmental factors affect tomato production, and at the same time, an experiment was conducted to investigate how well the yields of tomatoes could be predicted. From the results of the experiments, it was found that the proposed method predicts the response value using exogenous variables more efficiently and better than the existing models. In addition, we found that the environmental factors that greatly affect the yields of tomatoes are internal temperature, internal humidity, and CO2 level.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Forecasting of Tomato Yields Using Attention-Based LSTM Network and ARMA Model

Cho

Kim

et al. 2021

Electronics

View full text Add to dashboard Cite

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“…Likewise, Alkhoshi and Belkasim (2018) predicted the Dow Jones stock market index using the NARX neural network based on a training algorithm of LM, their results showed that the predicted model using NARX neural network provides high prediction accuracy based on the MSE score. Also, Wibowo et al (2017) predicted the price movement of the Indonesia composite index using the NARX neural network, their results showed that NARX neural network can be an alternative prediction model for investors and traders concerning the movements and dynamic variations of Indonesia composite index. Correspondingly, Das et al (2017) predicted the Dhaka stock price in the Malaysian stock market using the NARX neural model based on hybrid clustering, their results indicated that the NARX neural network was very efficient in predicting the Dhaka stock price by improving the error rate.…”

Section: Literature Reviewmentioning

confidence: 99%

“…As reported by Peng and Tang (2020), one of the modern ANNs that employed exogenous factors of the symmetric volatility as inputs to predict daily stock prices is the nonlinear autoregressive exogenous (NARX) neural network. As well, Wibowo et al (2017) determined that the NARX neural network provided high accuracy in predicting the capital market movements in Indonesia. Therefore, this study is using the NARX neural model to predict JKII prices based on symmetric volatility information.…”

Section: Introductionmentioning

confidence: 95%

Deep learning with small and big data of symmetric volatility information for predicting daily accuracy improvement of JKII prices

Ledhem¹

2022

JCMS

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PurposeThe purpose of this paper is to predict the daily accuracy improvement for the Jakarta Islamic Index (JKII) prices using deep learning (DL) with small and big data of symmetric volatility information.Design/methodology/approachThis paper uses the nonlinear autoregressive exogenous (NARX) neural network as the optimal DL approach for predicting daily accuracy improvement through small and big data of symmetric volatility information of the JKII based on the criteria of the highest accuracy score of testing and training. To train the neural network, this paper employs the three DL techniques, namely Levenberg–Marquardt (LM), Bayesian regularization (BR) and scaled conjugate gradient (SCG).FindingsThe experimental results show that the optimal DL technique for predicting daily accuracy improvement of the JKII prices is the LM training algorithm based on using small data which provide superior prediction accuracy to big data of symmetric volatility information. The LM technique develops the optimal network solution for the prediction process with 24 neurons in the hidden layer across a delay parameter equal to 20, which affords the best predicting accuracy based on the criteria of mean squared error (MSE) and correlation coefficient.Practical implicationsThis research would fill a literature gap by offering new operative techniques of DL to predict daily accuracy improvement and reduce the trading risk for the JKII prices based on symmetric volatility information.Originality/valueThis research is the first that predicts the daily accuracy improvement for JKII prices using DL with symmetric volatility information.

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“…Besides, (Indera et al, 2017) used NARX with Particle Swarm Optimization (PSO) to predict bitcoin price. Likewise, work by (Wibowo et al, 2017) studied the best structures of NARX for Indonesia composite index (IHSG)'s prediction. Also, research by (Alkhoshi & Belkasim, 2018) employed the NARX neural network, to predict the stock market index for the Dow Jones.…”

Section: Introductionmentioning

confidence: 99%

Input Parameters Comparison on NARX Neural Network to Increase the Accuracy of Stock Prediction

Agung¹

2022

JITE

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The trading of stocks is one of the activities carried out all over the world. To make the most profit, analysis is required, so the trader could determine whether to buy or sell stocks at the right moment and at the right price. Traditionally, technical analysis which is mathematically processed based on historical price data can be used. Parallel to technological development, the analysis of stock price and its forecasting can also be accomplished by using computer algorithms e.g. machine learning. In this study, Nonlinear Auto Regressive network with eXogenous inputs (NARX) neural network simulations were performed to predict the stock index prices. Experiments were implemented using various configurations of input parameters consisting of Open, High, Low, Closed prices in conjunction with several technical indicators for maximum accuracy. The simulations were carried out by using stock index data sets namely JKSE (Indonesia Jakarta index) and N225 (Japan Nikkei index). This work showed that the best input configurations can predict the future 13 days Close prices with 0.016 and 0.064 mean absolute error (MAE) for JKSE and N225 respectively.

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Nonlinear autoregressive exogenous model (NARX) in stock price index's prediction

Cited by 6 publications

References 5 publications

Forecasting of Tomato Yields Using Attention-Based LSTM Network and ARMA Model

Forecasting of Tomato Yields Using Attention-Based LSTM Network and ARMA Model

Deep learning with small and big data of symmetric volatility information for predicting daily accuracy improvement of JKII prices

Input Parameters Comparison on NARX Neural Network to Increase the Accuracy of Stock Prediction

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