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Journal articles on the topic 'Nonlinear Autoregressive model'

Author: Grafiati

Published: 14 March 2023

Last updated: 31 July 2025

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1

Meitz, Mika, and Pentti Saikkonen. "PARAMETER ESTIMATION IN NONLINEAR AR–GARCH MODELS." Econometric Theory 27, no. 6 (2011): 1236–78. http://dx.doi.org/10.1017/s0266466611000041.

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This paper develops an asymptotic estimation theory for nonlinear autoregressive models with conditionally heteroskedastic errors. We consider a general nonlinear autoregression of order p (AR(p)) with the conditional variance specified as a general nonlinear first-order generalized autoregressive conditional heteroskedasticity (GARCH(1,1)) model. We do not require the rescaled errors to be independent, but instead only to form a stationary and ergodic martingale difference sequence. Strong consistency and asymptotic normality of the global Gaussian quasi-maximum likelihood (QML) estimator are

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Kresnawati, Gayuh, Budi Warsito, and Abdul Hoyyi. "PERAMALAN INDEKS HARGA SAHAM GABUNGAN DENGAN METODE LOGISTIC SMOOTH TRANSITION AUTOREGRESSIVE (LSTAR)." Jurnal Gaussian 7, no. 1 (2018): 84–95. http://dx.doi.org/10.14710/j.gauss.v7i1.26638.

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Smooth Transition Autoregressive (STAR) Model is one of time series model used in case of data that has nonlinear tendency. STAR is an expansion of Autoregressive (AR) Model and can be used if the nonlinear test is accepted. If the transition function G(st,γ,c) is logistic, the method used is Logistic Smooth Transition Autoregressive (LSTAR). Weekly IHSG data in period of 3 January 2010 until 24 December 2017 has nonlinier tend and logistic transition function so it can be modeled with LSTAR . The result of this research with significance level of 5% is the LSTAR(1,1) model. The forecast of IH

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3

Sheng Lu and Ki H. Chon. "Nonlinear autoregressive and nonlinear autoregressive moving average model parameter estimation by minimizing hypersurface distance." IEEE Transactions on Signal Processing 51, no. 12 (2003): 3020–26. http://dx.doi.org/10.1109/tsp.2003.818999.

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4

Bauldry, Shawn, and Kenneth A. Bollen. "Nonlinear Autoregressive Latent Trajectory Models." Sociological Methodology 48, no. 1 (2018): 269–302. http://dx.doi.org/10.1177/0081175018789441.

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Autoregressive latent trajectory (ALT) models combine features of latent growth curve models and autoregressive models into a single modeling framework. The development of ALT models has focused primarily on models with linear growth components, but some social processes follow nonlinear trajectories. Although it is straightforward to extend ALT models to allow for some forms of nonlinear trajectories, the identification status of such models, approaches to comparing them with alternative models, and the interpretation of parameters have not been systematically assessed. In this paper we focus

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Srinivasan, Sundararajan, Tao Ma, Georgios Lazarou, and Joseph Picone. "A nonlinear autoregressive model for speaker verification." International Journal of Speech Technology 17, no. 1 (2013): 17–25. http://dx.doi.org/10.1007/s10772-013-9201-9.

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6

Ikoma, Norikazu, and Kaoru Hirota. "Nonlinear autoregressive model based on fuzzy relation." Information Sciences 71, no. 1-2 (1993): 131–44. http://dx.doi.org/10.1016/0020-0255(93)90068-w.

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7

Han, Xu, Huoyue Xiang, Yongle Li, and Yichao Wang. "Predictions of vertical train-bridge response using artificial neural network-based surrogate model." Advances in Structural Engineering 22, no. 12 (2019): 2712–23. http://dx.doi.org/10.1177/1369433219849809.

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To improve the efficiency of reliability calculations for vehicle-bridge systems, we present a surrogate modeling method based on a nonlinear autoregressive with exogenous input artificial neural network model and an important sample, which can forecast responses of dynamic systems, such as vehicle-bridge systems, subjected to stochastic excitations. We also propose a process to analyze the method. A quarter-vehicle model is used to verify the proposed method’s precision, and the nonlinear autoregressive with exogenous input artificial neural network model is used to predict responses of verti

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Wang, Meiqi, Enli Chen, Pengfei Liu, and Wenwu Guo. "Multivariable nonlinear predictive control of a clinker sintering system at different working states by combining artificial neural network and autoregressive exogenous." Advances in Mechanical Engineering 12, no. 1 (2020): 168781401989650. http://dx.doi.org/10.1177/1687814019896509.

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The clinker sintering system is widely controlled manually in the factory, and there is a large divergence between a linearized control model and the nonlinear rotary kiln system, so the controlled variables cannot be calculated accurately. To accommodate the multivariable and nonlinear features of cement clinker sintering systems, steady-state model and dynamic models are established using extreme learning machine and autoregressive exogenous models. The steady-state model is used to describe steady-state nonlinear relations, and the dynamic model is used to describe the dynamic characteristi

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9

Xiong, Weili, Wei Fan, and Rui Ding. "Least-Squares Parameter Estimation Algorithm for a Class of Input Nonlinear Systems." Journal of Applied Mathematics 2012 (2012): 1–14. http://dx.doi.org/10.1155/2012/684074.

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This paper studies least-squares parameter estimation algorithms for input nonlinear systems, including the input nonlinear controlled autoregressive (IN-CAR) model and the input nonlinear controlled autoregressive autoregressive moving average (IN-CARARMA) model. The basic idea is to obtain linear-in-parameters models by overparameterizing such nonlinear systems and to use the least-squares algorithm to estimate the unknown parameter vectors. It is proved that the parameter estimates consistently converge to their true values under the persistent excitation condition. A simulation example is

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10

Yang, Xiao-Hua, and Yu-Qi Li. "DNA Optimization Threshold Autoregressive Prediction Model and Its Application in Ice Condition Time Series." Mathematical Problems in Engineering 2012 (2012): 1–10. http://dx.doi.org/10.1155/2012/191902.

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There are many parameters which are very difficult to calibrate in the threshold autoregressive prediction model for nonlinear time series. The threshold value, autoregressive coefficients, and the delay time are key parameters in the threshold autoregressive prediction model. To improve prediction precision and reduce the uncertainties in the determination of the above parameters, a new DNA (deoxyribonucleic acid) optimization threshold autoregressive prediction model (DNAOTARPM) is proposed by combining threshold autoregressive method and DNA optimization method. The above optimal parameters

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11

Min, Chen, and An Hongzhi. "The existence of moments of nonlinear autoregressive model." Acta Mathematicae Applicatae Sinica 14, no. 3 (1998): 328–32. http://dx.doi.org/10.1007/bf02677414.

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12

Yokota, Yasunari, Masaomi Koizumi, and Noritaka Matsuoka. "Prediction coding of ECG by nonlinear autoregressive model." Systems and Computers in Japan 31, no. 7 (2000): 66–74. http://dx.doi.org/10.1002/(sici)1520-684x(200007)31:7<66::aid-scj8>3.0.co;2-q.

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13

Hou, Dehao, Wenjun Ma, Lingyan Hu, et al. "Modeling of Nonlinear SOEC Parameter System Based on Data-Driven Method." Atmosphere 14, no. 9 (2023): 1432. http://dx.doi.org/10.3390/atmos14091432.

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Based on the basic nonlinear parameter system of the solid oxide electrolysis cell, the data-driven method was used for system identification. The basic model of the solid oxide electrolysis cell was accomplished in Simulink and experiments were performed under a diversified input/output operating environment. The experimental results of the solid oxide electrolysis cell basic parameter system generated 15 datasets. The system identification process involved the utilization of these datasets with the application of nonlinear autoregressive-exogenous models. Initially, data identification came

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14

Sapra, Sunil. "A comparative study of parametric and semiparametric autoregressive models." International Journal of Accounting and Economics Studies 10, no. 1 (2022): 15–19. http://dx.doi.org/10.14419/ijaes.v10i1.31978.

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Dynamic linear regression models are used widely in applied econometric research. Most applications employ linear autoregressive (AR) models, distributed lag (DL) models or autoregressive distributed lag (ARDL) models. These models, however, perform poorly for data sets with unknown, complex nonlinear patterns. This paper studies nonlinear and semiparametric extensions of the dynamic linear regression model and explores the autoregressive (AR) extensions of two semiparametric techniques to allow unknown forms of nonlinearities in the regression function. The autoregressive GAM (GAM-AR) and aut

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15

Blanchard, Tyler, and Biswanath Samanta. "Wind speed forecasting using neural networks." Wind Engineering 44, no. 1 (2019): 33–48. http://dx.doi.org/10.1177/0309524x19849846.

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The prediction of wind speed is critical in the assessment of feasibility of a potential wind turbine site. This work presents a study on prediction of wind speed using artificial neural networks. Two variations of artificial neural networks, namely, nonlinear autoregressive neural network and nonlinear autoregressive neural network with exogenous inputs, were used to predict wind speed utilizing 1 year of hourly weather data from four locations around the United States to train, validate, and test these networks. This study optimized both neural network configurations and it demonstrated that

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16

Hermansah, Hermansah, Dedi Rosadi, Abdurakhman Abdurakhman, and Herni Utami. "SELECTION OF INPUT VARIABLES OF NONLINEAR AUTOREGRESSIVE NEURAL NETWORK MODEL FOR TIME SERIES DATA FORECASTING." MEDIA STATISTIKA 13, no. 2 (2020): 116–24. http://dx.doi.org/10.14710/medstat.13.2.116-124.

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NARNN is a type of ANN model consisting of a limited number of parameters and widely used for various applications. This study aims to determine the appropriate NARNN model, for the selection of input variables of nonlinear autoregressive neural network model for time series data forecasting, using the stepwise method. Furthermore, the study determines the optimal number of neurons in the hidden layer, using a trial and error method for some architecture. The NARNN model is combined in three parts, namely the learning method, the activation function, and the ensemble operator, to get the best

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17

Su, Liyun, and Chenlong Li. "Local Prediction of Chaotic Time Series Based on Polynomial Coefficient Autoregressive Model." Mathematical Problems in Engineering 2015 (2015): 1–14. http://dx.doi.org/10.1155/2015/901807.

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We apply the polynomial function to approximate the functional coefficients of the state-dependent autoregressive model for chaotic time series prediction. We present a novel local nonlinear model called local polynomial coefficient autoregressive prediction (LPP) model based on the phase space reconstruction. The LPP model can effectively fit nonlinear characteristics of chaotic time series with simple structure and have excellent one-step forecasting performance. We have also proposed a kernel LPP (KLPP) model which applies the kernel technique for the LPP model to obtain better multistep fo

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18

Goryainov, A. V., V. B. Goryainov, and W. M. Khing. "Robust Identification of an Exponential Autoregressive Model." Herald of the Bauman Moscow State Technical University. Series Natural Sciences, no. 4 (91) (August 2020): 42–57. http://dx.doi.org/10.18698/1812-3368-2020-4-42-57.

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One of the most common nonlinear time series (random processes with discrete time) models is the exponential autoregressive model. In particular, it describes such nonlinear effects as limit cycles, resonant jumps, and dependence of the oscillation frequency on amplitude. When identifying this model, the problem arises of estimating its parameters --- the coefficients of the corresponding autoregressive equation. The most common methods for estimating the parameters of an exponential model are the least squares method and the least absolute deviation method. Both of these methods have a number

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19

Syed Asad, Hussain, Yuen Richard Kwok Kit, and Lee Eric Wai Ming. "Energy Modeling with Nonlinear-Autoregressive Exogenous Neural Network." E3S Web of Conferences 111 (2019): 03059. http://dx.doi.org/10.1051/e3sconf/201911103059.

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The model-based predictive control (MPC) is considered to be an effective tool for optimal control of building heating, ventilation, and air-conditioning (HVAC) systems. MPC need to update the operating set points of the local control loops that have a significant influence on the energy performance of the system. Performance of MPC relies on the accuracy of the system performance model. There are two commonly used modeling approach – conventional or analytical approach that is the way of process modeling for some time, but it tends to increase the online computational load as it requires a fu

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20

Yu, Pen-Ning, Charles Y. Liu, Christianne N. Heck, Theodore W. Berger, and Dong Song. "A sparse multiscale nonlinear autoregressive model for seizure prediction." Journal of Neural Engineering 18, no. 2 (2021): 026012. http://dx.doi.org/10.1088/1741-2552/abdd43.

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21

Kato, Hiroko, and Tohru Ozaki. "Adding data process feedback to the nonlinear autoregressive model." Signal Processing 82, no. 9 (2002): 1189–204. http://dx.doi.org/10.1016/s0165-1684(02)00139-1.

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22

Li, Junxing, Zhihua Wang, Yongbo Zhang, Chengrui Liu, and Huimin Fu. "A nonlinear Wiener process degradation model with autoregressive errors." Reliability Engineering & System Safety 173 (May 2018): 48–57. http://dx.doi.org/10.1016/j.ress.2017.11.003.

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23

Vesin, J. M. "A nonlinear autoregressive signal model with state-dependent gain." Signal Processing 26, no. 1 (1992): 37–48. http://dx.doi.org/10.1016/0165-1684(92)90054-z.

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24

Bai, Yu-ting, Xiao-yi Wang, Xue-bo Jin, Zhi-yao Zhao, and Bai-hai Zhang. "A Neuron-Based Kalman Filter with Nonlinear Autoregressive Model." Sensors 20, no. 1 (2020): 299. http://dx.doi.org/10.3390/s20010299.

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The control effect of various intelligent terminals is affected by the data sensing precision. The filtering method has been the typical soft computing method used to promote the sensing level. Due to the difficult recognition of the practical system and the empirical parameter estimation in the traditional Kalman filter, a neuron-based Kalman filter was proposed in the paper. Firstly, the framework of the improved Kalman filter was designed, in which the neuro units were introduced. Secondly, the functions of the neuro units were excavated with the nonlinear autoregressive model. The neuro un

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25

Pereira, Janser Moura, Joel Augusto Muniz, and Carlos Alberto Silva. "Nonlinear models to predict nitrogen mineralization in an Oxisol." Scientia Agricola 62, no. 4 (2005): 395–400. http://dx.doi.org/10.1590/s0103-90162005000400014.

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This work was carried out to evaluate the statistical properties of eight nonlinear models used to predict nitrogen mineralization in soils of the Southern Minas Gerais State, Brazil. The parameter estimations for nonlinear models with and without structure of autoregressive errors was made by the least squares method. First, a structure of second order autoregressive errors, AR(2) was considered for all nonlinear models and then the significance of the autocorrelation parameters was verified. Among the models, the Juma presented an autocorrelation of second order, and the model of Broadbent p

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26

Xu, Wenquan, and Hui Hu. "A Novel FRBF-Type Model for Nonlinear Time Series Prediction." Mathematical Problems in Engineering 2023 (April 17, 2023): 1–14. http://dx.doi.org/10.1155/2023/5753023.

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Accurate prediction of time series is complex due to nonlinear characteristics but can play a significant role in practical problem. In this paper, a novel varying-coefficient hybrid model is proposed to accurately predict the nonlinear time series. A set of fuzzy radial basis function (FRBF) neural networks is used to approximate the varying functional coefficients of the state-dependent autoregressive model with exogenous variables (SD-ARX). The obtained model is called the fuzzy radial basis function network-based autoregressive model with exogenous variables (FRBF-ARX), which combines the

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27

CHONG, TERENCE TAI-LEUNG, TAU-HING LAM, and MELVIN J. HINICH. "ARE NONLINEAR TRADING RULES PROFITABLE IN THE CHINESE STOCK MARKET?" Annals of Financial Economics 05, no. 01 (2009): 0950002. http://dx.doi.org/10.1142/s201049520950002x.

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The rise of China in the world economy has attracted a great deal of international attention. This paper investigates the performance of nonlinear self-exciting threshold autoregressive (SETAR) model-based trading rules in the Chinese stock market. We compare the performance of the SETAR model with the autoregressive (AR) model and the moving average (MA) trading rules. Our results indicate that trading rules are profitable in the B-share market, and that the nonlinear SETAR rule outperforms the other two linear rules in general.

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28

Ma, Qingwen, Sihan Liu, Xinyu Fan, Chen Chai, Yangyang Wang, and Ke Yang. "A Time Series Prediction Model of Foundation Pit Deformation Based on Empirical Wavelet Transform and NARX Network." Mathematics 8, no. 9 (2020): 1535. http://dx.doi.org/10.3390/math8091535.

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Large deep foundation pits are usually in a complex environment, so their surface deformation tends to show a stable rising trend with a small range of fluctuation, which brings certain difficulty to the prediction work. Therefore, in this study we proposed a nonlinear autoregressive exogenous (NARX) prediction method based on empirical wavelet transform (EWT) pretreatment is proposed for this feature. Firstly, EWT is used to conduct adaptive decomposition of the measured deformation data and extract the modal signal components with characteristic differences. Secondly, the main components aff

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29

Jibrin, Sanusi Alhaji, Abdulhameed Ado Osi, and Shukurana Shehu. "Developing Exp-FIGARCH Hybrid Models for Time Series Modelling." Dutse Journal of Pure and Applied Sciences 10, no. 1c (2024): 83–95. http://dx.doi.org/10.4314/dujopas.v10i1c.8.

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In this paper, we introduced a new hybrid model namely Exponential Autoregressive-Fractional Integrated Generalized Autoregressive Conditional Heteroscedasticity (ExpAR-FIGARCH) model and study financial data. The Daily Nigeria All Share Stock Index that exhibit nonlinear, volatility and long memory effect were analyzed in the study. The existing ExpAR-Generalized Autoregressive Conditional Heteroscedasticity (ExpAR-GARCH) model were estimated and compared with the proposed ExpAR-FIGARCHmodel. Results showed that the new hybrid model is better based on efficient parameters, serial correlation

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30

Zena. S. Khalaf and Azher. A. Mohammad. "On stability Conditions of Burr X Autoregressive model." Tikrit Journal of Pure Science 24, no. 5 (2019): 91–96. http://dx.doi.org/10.25130/tjps.v24i5.423.

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This article deals with proposed nonlinear autoregressive model based on Burr X cumulative distribution function known as Burr X AR (p), we demonstrate stability conditions of the proposed model in terms of its parameters by using dynamical approach known as local linearization method to find stability conditions of a nonzero fixed point of the proposed model, in addition the study demonstrate stability condition of a limit cycle if Burr X AR (1) model have a limit cycle of period greater than one.

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31

Khrisat, Mohammad S., Anwar Alabadi, Saleh Khawatreh, Majed Omar Al-Dwairi, and Ziad A. Alqadi. "Autoregressive prediction analysis using machine deep learning." Indonesian Journal of Electrical Engineering and Computer Science 27, no. 3 (2022): 1509–15. https://doi.org/10.11591/ijeecs.v27.i3.pp1509-1515.

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Regression analysis, in statistic a modelling, is a set of statical processes that can be used to estimate the relationship between a dependent variable, commonly known as the outcome or response, and more independent variables generally called predictors of covariant. On the other hand, autoregression, which is based on regression equations, is a sequential model that uses time to predict the next step data from the previous step. Given the importance of accurate modelling and reliable predictions. in this paper we have analyzed the most popular methods used for data prediction. Nonlinear aut

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32

S. Khalaf, Zena, and ,. Azher A. Mohammad. "On stability Conditions of Burr X Autoregressive model." Tikrit Journal of Pure Science 24, no. 5 (2019): 91. http://dx.doi.org/10.25130/j.v24i5.873.

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This article deals with proposed nonlinear autoregressive model based on Burr X cumulative distribution function known as Burr X AR (p), we demonstrate stability conditions of the proposed model in terms of its parameters by using dynamical approach known as local linearization method to find stability conditions of a nonzero fixed point of the proposed model, in addition the study demonstrate stability condition of a limit cycle if Burr X AR (1) model have a limit cycle of period greater than one.  http://dx.doi.org/10.25130/tjps.24.2019.096

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33

Fueda, Kaoru. "AN ADAPTIVE VARIABLE SELECTION FOR NONLINEAR AUTOREGRESSIVE TIME SERIES MODEL." Bulletin of informatics and cybernetics 37 (December 2005): 109–21. http://dx.doi.org/10.5109/12594.

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Hwang, Eunju, and Dong Wan Shin. "Stationary bootstrapping for non-parametric estimator of nonlinear autoregressive model." Journal of Time Series Analysis 32, no. 3 (2010): 292–303. http://dx.doi.org/10.1111/j.1467-9892.2010.00699.x.

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35

Elhassanein, A. "On the Control of Forced Process Feedback Nonlinear Autoregressive Model." Journal of Computational and Theoretical Nanoscience 12, no. 8 (2015): 1519–26. http://dx.doi.org/10.1166/jctn.2015.3923.

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Hajrajabi, Arezo. "Markov switching model of nonlinear autoregressive with skew-symmetric innovations." Journal of Statistical Computation and Simulation 89, no. 4 (2019): 559–75. http://dx.doi.org/10.1080/00949655.2018.1563089.

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Eikenberry, Steffen E., and Vasilis Z. Marmarelis. "A nonlinear autoregressive Volterra model of the Hodgkin–Huxley equations." Journal of Computational Neuroscience 34, no. 1 (2012): 163–83. http://dx.doi.org/10.1007/s10827-012-0412-x.

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38

CHELANI, A., and S. DEVOTTA. "Air quality forecasting using a hybrid autoregressive and nonlinear model." Atmospheric Environment 40, no. 10 (2006): 1774–80. http://dx.doi.org/10.1016/j.atmosenv.2005.11.019.

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39

Bagnato, Luca, and Antonio Punzo. "Nonparametric bootstrap test for autoregressive additive models." Statistics in Transition new series 10, no. 3 (2009): 359–70. http://dx.doi.org/10.59170/stattrans-2009-027.

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Additive autoregressive models are commonly used to describe and simplify the behaviour of a nonlinear time series. When the additive structure is chosen, and the model estimated, it is important to evaluate if it is really suitable to describe the observed data since additivity represents a strong assumption. Although literature presents extensive developments on additive autoregressive models, few are the methods to test additivity which are generally applicable. In this paper a procedure for testing additivity in nonlinear time series analysis is provided. The method is based on: Generalize

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Solikhah, Arifatus, Heri Kuswanto, Nur Iriawan, and Kartika Fithriasari. "Fisher’s z Distribution-Based Mixture Autoregressive Model." Econometrics 9, no. 3 (2021): 27. http://dx.doi.org/10.3390/econometrics9030027.

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We generalize the Gaussian Mixture Autoregressive (GMAR) model to the Fisher’s z Mixture Autoregressive (ZMAR) model for modeling nonlinear time series. The model consists of a mixture of K-component Fisher’s z autoregressive models with the mixing proportions changing over time. This model can capture time series with both heteroskedasticity and multimodal conditional distribution, using Fisher’s z distribution as an innovation in the MAR model. The ZMAR model is classified as nonlinearity in the level (or mode) model because the mode of the Fisher’s z distribution is stable in its location p

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Noviandari, Puji, and Renny Renny. "KAJIAN PEMODELAN DERET WAKTU NONLINIER THRESHOLD AUTOREGRESSIVE (TAR)." Jurnal Ilmiah Matematika dan Pendidikan Matematika 4, no. 1 (2012): 123. http://dx.doi.org/10.20884/1.jmp.2012.4.1.2947.

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Nonlinear time series are time series that are not stable due to a sudden jump. Nonlinear time series often found in financial data. Threshold Autoregressive (TAR) modeling is a time series modeling with a segmented autoregressive (AR)’s model such that among different regimes may have different AR model. This research studied how to obtain the Ordinary Least Square (OLS) estimator for TAR model and examine signification the OLS’s estimator by using t test. This research also studied the other stages of TAR modeling, which are nonlinearity test using Tsay test, TAR model identification by usin

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42

Ripatti, Antti, and Pentti. "VECTOR AUTOREGRESSIVE PROCESSES WITH NONLINEAR TIME TRENDS IN COINTEGRATING RELATIONS." Macroeconomic Dynamics 5, no. 4 (2001): 577–97. http://dx.doi.org/10.1017/s1365100501023069.

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We extend the conventional cointegrated VAR model to allow for general nonlinear deterministic trends. These nonlinear trends can be used to model gradual structural changes in the intercept term of the cointegrating relations. A general asymptotic theory of estimation and statistical inference is reviewed and a diagnostic test for the correct specification of an employed nonlinear trend is developed. The methods are applied to Finnish interest-rate data. A smooth level shift of the logistic form between the own-yield of broad money and the short-term money market rate is found appropriate for

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43

Hesamian, Gholamreza, Faezeh Torkian, Arne Johannssen, and Nataliya Chukhrova. "An Exponential Autoregressive Time Series Model for Complex Data." Mathematics 11, no. 19 (2023): 4022. http://dx.doi.org/10.3390/math11194022.

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In this paper, an exponential autoregressive model for complex time series data is presented. As for estimating the parameters of this nonlinear model, a three-step procedure based on quantile methods is proposed. This quantile-based estimation technique has the benefit of being more robust compared to least/absolute squares. The performance of the introduced exponential autoregressive model is evaluated by means of four established goodness-of-fit criteria. The practical utility of the novel time series model is showcased through a comparative analysis involving simulation studies and real-wo

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Prayuda, Edwan Dio, Netti Herawati, Dorrah Aziz, and Nusyirwan. "PREDICTING INFLATION INDONESIA USING NONLINEAR TIME SERIES MODEL: A COMPARATIVE STUDY." International Journal of Applied Science and Engineering Review 05, no. 04 (2024): 10–22. http://dx.doi.org/10.52267/ijaser.2024.5402.

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This study aims to compare the performance of Self-Exciting Threshold Autoregressive (SETAR) and Markov Switching Autoregressive (MSAR) models in modeling and predicting inflation data in Indonesia. Inflation is one of the important economic indicators that requires accurate forecasting methods to make the right policy decisions. In this study, both models are applied to monthly inflation data in Indonesia. The SETAR model is a nonlinear autoregressive model that takes into account regime changes in the inflation rate based on a certain threshold value. Meanwhile, the MSAR model assumes that i

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El Hamidi, Khadija, Mostafa Mjahed, Abdeljalil El Kari, and Hassan Ayad. "Adaptive Control Using Neural Networks and Approximate Models for Nonlinear Dynamic Systems." Modelling and Simulation in Engineering 2020 (August 26, 2020): 1–13. http://dx.doi.org/10.1155/2020/8642915.

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In this research, a comparative study of two recurrent neural networks, nonlinear autoregressive with exogenous input (NARX) neural network and nonlinear autoregressive moving average (NARMA-L2), and a feedforward neural network (FFNN) is performed for their ability to provide adaptive control of nonlinear systems. Three dynamical nonlinear systems of different complexity are considered. The aim of this work is to make the output of the plant follow the desired reference trajectory. The problem becomes more challenging when the dynamics of the plants are assumed to be unknown, and to tackle th

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Nadirah Mohd Johari, Sarah, Fairuz Husna Muhamad Farid, Nur Afifah Enara Binti Nasrudin, Nur Sarah Liyana Bistamam, and Nur Syamira Syamimi Muhammad Shuhaili. "Predicting Stock Market Index Using Hybrid Intelligence Model." International Journal of Engineering & Technology 7, no. 3.15 (2018): 36. http://dx.doi.org/10.14419/ijet.v7i3.15.17403.

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Predicting financial market changes is an important issue in time series analysis, receiving an increasing attention due to financial crisis. Autoregressive integrated moving average (ARIMA) model has been one of the most widely used linear models in time series forecasting but ARIMA model cannot capture nonlinear patterns easily. Generalized autoregressive conditional heteroscedasticity (GARCH) model applied understanding of volatility depending to the estimation of previous forecast error and current volatility, improving ARIMA model. Support vector machine (SVM) and artificial neural networ

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Hu, Huiyi, Xiao Yongsong, and Rui Ding. "Multi-Innovation Stochastic Gradient Identification Algorithm for Hammerstein Controlled Autoregressive Autoregressive Systems Based on the Key Term Separation Principle and on the Model Decomposition." Journal of Applied Mathematics 2013 (2013): 1–7. http://dx.doi.org/10.1155/2013/596141.

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An input nonlinear system is decomposed into two subsystems, one including the parameters of the system model and the other including the parameters of the noise model, and a multi-innovation stochastic gradient algorithm is presented for Hammerstein controlled autoregressive autoregressive (H-CARAR) systems based on the key term separation principle and on the model decomposition, in order to improve the convergence speed of the stochastic gradient algorithm. The key term separation principle can simplify the identification model of the input nonlinear system, and the decomposition technique

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Sousa, Iábita Fabiana, Johan Eugen Kunzle Neto, Joel Augusto Muniz, Renato Mendes Guimarães, Taciana Villela Savian, and Fabiana Rezende Muniz. "Fitting nonlinear autoregressive models to describe coffee seed germination." Ciência Rural 44, no. 11 (2014): 2016–21. http://dx.doi.org/10.1590/0103-8478cr20131341.

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Cumulative germination of coffee has a longitudinal behavior mathematically characterized by a sigmoidal model. In the seed germination evaluation, the study of the germination curve may contribute to better understanding of this process. The aim of this study was to evaluate the goodness of fit of Logistic and Gompertz models, with independent and first-order autoregressive errors structure, AR (1), in the description of coffee (Coffea arabica L.) line Catuai vermelho IAC 99 germination, at five different potential germination. The data used were from an experiment conducted in 2011 at the Se

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Zheng, Dawei. "Prediction of the Length of Day from Atmospheric Angular Momentum with LSTAR Model." Symposium - International Astronomical Union 156 (1993): 335. http://dx.doi.org/10.1017/s0074180900173449.

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Adopting the time series of atmospheric angular momentum (AAM) from the National Meteorological Center of USA, the study of the prediction of the length of day (LOD) has been made by the Leap-Step Threshold AutoRegressive (LSTAR) model. The LSTAR model presented by the author is a sort of models for nonlinear time series analysis such as where Dj is the j-th leap-step domain of the data series Zn, and (j) if the sample number N=L×M, then Zj+(L×K) εDj and K=0,1,…,M−1. En denotes the white noise of data in the j-th leap-step domain. TSM denotes a class of models in time series analysis and the n

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Rebora, Nicola, Luca Ferraris, Jost von Hardenberg, and Antonello Provenzale. "RainFARM: Rainfall Downscaling by a Filtered Autoregressive Model." Journal of Hydrometeorology 7, no. 4 (2006): 724–38. http://dx.doi.org/10.1175/jhm517.1.

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Abstract A method is introduced for stochastic rainfall downscaling that can be easily applied to the precipitation forecasts provided by meteorological models. Our approach, called the Rainfall Filtered Autoregressive Model (RainFARM), is based on the nonlinear transformation of a Gaussian random field, and it conserves the information present in the rainfall fields at larger scales. The procedure is tested on two radar-measured intense rainfall events, one at midlatitude and the other in the Tropics, and it is shown that the synthetic fields generated by RainFARM have small-scale statistical

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