Relevant bibliographies by topics / Nonlinear Autoregressive model

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Nonlinear Autoregressive model'

Author: Grafiati
Published: 14 March 2023
Last updated: 31 July 2025

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Journal articles on the topic "Nonlinear Autoregressive model"

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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Nonlinear Autoregressive model"

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Uysal, Ela. "Application Of Nonlinear Unit Root Tests And Threshold Autoregressive Models." Master's thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12614878/index.pdf.

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Popularity of nonlinear threshold models and unit root tests has increased after the recent empirical studies concerning the effects of business cycles on macroeconomic data. These studies have shown that an economic variable may react differently in response to downturns and recoveries in a business cycle. Inspiring from empirical results, this thesis investigates dynamics of Turkish key macroeconomic data, namely capacity utilization rate, growth of import and export volume indices, growth of gross domestic product, interest rate for cash loans in Turkish Liras and growth of industrial produ
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Rech, Gianluigi. "Modelling and forecasting economic time series with single hidden-layer feedforward autoregressive artificial neural networks." Doctoral thesis, Handelshögskolan i Stockholm, Ekonomisk Statistik (ES), 2001. http://urn.kb.se/resolve?urn=urn:nbn:se:hhs:diva-591.

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This dissertation consists of 3 essays In the first essay, A Simple Variable Selection Technique for Nonlinear Models, written in cooperation with Timo Teräsvirta and Rolf Tschernig, I propose a variable selection method based on a polynomial expansion of the unknown regression function and an appropriate model selection criterion. The hypothesis of linearity is tested by a Lagrange multiplier test based on this polynomial expansion. If rejected, a kth order general polynomial is used as a base for estimating all submodels by ordinary least squares. The combination of regressors leading to the
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Ogbonna, Emmanuel. "A multi-parameter empirical model for mesophilic anaerobic digestion." Thesis, University of Hertfordshire, 2017. http://hdl.handle.net/2299/17467.

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Anaerobic digestion, which is the process by which bacteria breakdown organic matter to produce biogas (renewable energy source) and digestate (biofertiliser) in the absence of oxygen, proves to be the ideal concept not only for sustainable energy provision but also for effective organic waste management. However, the production amount of biogas to keep up with the global demand is limited by the underperformance in the system implementing the AD process. This underperformance is due to the difficulty in obtaining and maintaining the optimal operating parameters/states for anaerobic bacteria t
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Dupré, la Tour Tom. "Nonlinear models for neurophysiological time series." Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLT018/document.

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Dans les séries temporelles neurophysiologiques, on observe de fortes oscillations neuronales, et les outils d'analyse sont donc naturellement centrés sur le filtrage à bande étroite.Puisque cette approche est trop réductrice, nous proposons de nouvelles méthodes pour représenter ces signaux.Nous centrons tout d'abord notre étude sur le couplage phase-amplitude (PAC), dans lequel une bande haute fréquence est modulée en amplitude par la phase d'une oscillation neuronale plus lente.Nous proposons de capturer ce couplage dans un modèle probabiliste appelé modèle autoregressif piloté (DAR). Cette
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ALIEV, KHURSHID. "Internet of Things Applications and Artificial Neural Networks in Smart Agriculture." Doctoral thesis, Politecnico di Torino, 2018. http://hdl.handle.net/11583/2697287.

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Internet of Things (IoT) is receiving a great attention due to its potential strength and ability to be integrated into any complex systems and it is becoming a great tool to acquire data from particular environment to the cloud. Data that are acquired from Wireless Sensor Nodes(WSN) could be predicted using Artificial Neural Network(ANN) models. One of the use case fields of IoT is smart agriculture and there are still issues on developing low cost and power efficient WSN using advanced radio technologies for short and long-range applications and implementation of prediction tools. This is th
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Dupré, la Tour Tom. "Nonlinear models for neurophysiological time series." Electronic Thesis or Diss., Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLT018.

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Dans les séries temporelles neurophysiologiques, on observe de fortes oscillations neuronales, et les outils d'analyse sont donc naturellement centrés sur le filtrage à bande étroite.Puisque cette approche est trop réductrice, nous proposons de nouvelles méthodes pour représenter ces signaux.Nous centrons tout d'abord notre étude sur le couplage phase-amplitude (PAC), dans lequel une bande haute fréquence est modulée en amplitude par la phase d'une oscillation neuronale plus lente.Nous proposons de capturer ce couplage dans un modèle probabiliste appelé modèle autoregressif piloté (DAR). Cette
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Lee, Kian Lam. "Nonlinear time series modelling and prediction using polynomial and radial basis function expansions." Thesis, University of Sheffield, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.246940.

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Zhou, Jia. "SMOOTH TRANSITION AUTOREGRESSIVE MODELS : A STUDY OF THE INDUSTRIAL PRODUCTION INDEX OF SWEDEN." Thesis, Uppsala University, Department of Statistics, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-126752.

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<p>In this paper, we study the industrial production index of Sweden from Jan, 2000 to latest Feb, 2010. We find out there is a structural break at time point Dec, 2007, when the global financial crisis burst out first in U.S then spread to Europe. To model the industrial production index, one of the business cycle indicators which may behave nonlinear feature suggests utilizing a smooth transition autoregressive (STAR) model. Following the procedures given by Teräsvirta (1994), we carry out the linearity test against the STAR model, determine the delay parameter and choose between the LSTAR m
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Katsiampa, Paraskevi. "Nonlinear exponential autoregressive time series models with conditional heteroskedastic errors with applications to economics and finance." Thesis, Loughborough University, 2015. https://dspace.lboro.ac.uk/2134/18432.

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The analysis of time series has long been the subject of interest in different fields. For decades time series were analysed with linear models, which have many advantages. Nevertheless, an issue which has been raised is whether there exist other models that can explain and forecast real data better than linear ones. In this thesis, new nonlinear time series models are suggested, which consist of a nonlinear conditional mean model, such as an ExpAR or an Extended ExpAR, and a nonlinear conditional variance model, such as an ARCH or a GARCH. Since new models are introduced, simulated series of
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"Change point estimation for threshold autoregressive (TAR) model." 2012. http://library.cuhk.edu.hk/record=b5549066.

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時間序列之變點鬥檻模型是一種非線性的模型。此論文探討有關該模型之參數估計，同時對其參數估計作出統計分析。我們運用了遺傳式計算機運算來估計這些參數及對其作出研究。我們利用了MDL來對比不同的變點門檻模型，同時我們也利用了MDL來選取對應的變點門檻模型。<br>This article considers the problem of modeling non-linear time series by using piece-wise TAR model. The numbers of change points, the numbers of thresholds and the corresponding order of AR in each piecewise TAR segments are assumed unknown. The goal is to nd out the “best“ combination of the number of change points, the value of threshold in each time segment, and the underlying AR order for each threshold regime. A genetic algorithm is implemented to solve this op
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Books on the topic "Nonlinear Autoregressive model"

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Novikov, Anatoliy, Tat'yana Solodkaya, Aleksandr Lazerson, and Viktor Polyak. Econometric modeling in the GRETL package. INFRA-M Academic Publishing LLC., 2023. http://dx.doi.org/10.12737/1732940.

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The tutorial describes the capabilities of the GRETL statistical package for computer data analysis and econometric modeling based on spatial data and time series. Using concrete economic examples, GRETL considers classical and generalized models of linear and nonlinear regression, methods for detecting and eliminating multicollinearity, models with variable structure, autoregressive processes, methods for testing and eliminating autocorrelation, as well as discrete choice models and systems of simultaneous equations.&#x0D; For the convenience of users, the tutorial contains all the task data
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Book chapters on the topic "Nonlinear Autoregressive model"

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Castiglione, Juan, Rodrigo Astroza, Saeed Eftekhar Azam, and Daniel Linzell. "Output-Only Nonlinear Finite Element Model Updating Using Autoregressive Process." In Model Validation and Uncertainty Quantification, Volume 3. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-47638-0_9.

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Chhipa, Abrar Ahmed, Vinod Kumar, and R. R. Joshi. "Grid-Connected PV System Power Forecasting Using Nonlinear Autoregressive Exogenous Model." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-0193-5_10.

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Le, Tien-Thinh, Binh Thai Pham, Hai-Bang Ly, Ataollah Shirzadi, and Lu Minh Le. "Development of 48-hour Precipitation Forecasting Model using Nonlinear Autoregressive Neural Network." In Lecture Notes in Civil Engineering. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0802-8_191.

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Merzguioui, Mhamed El, Younes Ait Taleb, and Mustapha El Jarroudi. "ARCH Model and Nonlinear Autoregressive Neural Networks for Forecasting Financial Time Series." In Innovations in Smart Cities Applications Volume 6. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-26852-6_45.

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Horejš, Otakar, Martin Mareš, Michal Straka, Jiří Švéda, and Tomáš Kozlok. "Adaptive Thermal Error Compensation Model of a Horizontal Machining Centre." In Lecture Notes in Production Engineering. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-34486-2_7.

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AbstractThe state-of-the-art method to reduce CNC machine tool thermal errors is real-time error compensation based on the thermal error estimation models. However, it is difficult to establish a thermal error compensation model with good versatility, high accuracy, and strong robustness due to various manufacturing conditions and a thermally varying surrounding environment. It causes that thermal behaviour of the machine tools is nonlinear and varying in real time. Consequently, the pre-trained and non-adaptive model may not be accurate and robust enough for long-term application. The present
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Koul, Hira L. "Nonlinear Autoregression." In Weighted Empirical Processes in Dynamic Nonlinear Models. Springer New York, 2002. http://dx.doi.org/10.1007/978-1-4613-0055-7_8.

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Adenuga, Olukorede Tijani, Khumbulani Mpofu, and Ragosebo Kgaugelo Modise. "Application of ARIMA-LSTM for Manufacturing Decarbonization Using 4IR Concepts." In Lecture Notes in Mechanical Engineering. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-18326-3_12.

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AbstractIncreasing climate change concerns call for the manufacturing sector to decarbonize its process by introducing a mitigation strategy. Energy efficiency concepts within the manufacturing process value chain are proportional to the emission reductions, prompting decision makers to require predictive tools to execute decarbonization solutions. Accurate forecasting requires techniques with a strong capability for predicting automotive component manufacturing energy consumption and carbon emission data. In this paper we introduce a hybrid autoregressive moving average (ARIMA)-long short-ter
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Koul, Hira L. "Autoregression." In Weighted Empirical Processes in Dynamic Nonlinear Models. Springer New York, 2002. http://dx.doi.org/10.1007/978-1-4613-0055-7_7.

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Zhang, Lei. "Nonlinear Autoregressive Model Design and Optimization Based on ANN for the Prediction of Chaotic Patterns in EEG Time Series." In Biomedical Engineering and Computational Intelligence. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-21726-6_5.

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Teräsvirta, Timo. "Nonlinear Models for Autoregressive Conditional Heteroskedasticity." In Handbook of Volatility Models and Their Applications. John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781118272039.ch2.

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Conference papers on the topic "Nonlinear Autoregressive model"

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Yu, Jiaxin, Yining Dong, and S. Joe Qin. "Kernel Latent Vector Autoregressive Model for Nonlinear Dynamic Data Modeling and Monitoring." In 2024 IEEE 63rd Conference on Decision and Control (CDC). IEEE, 2024. https://doi.org/10.1109/cdc56724.2024.10885978.

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Brands, Ren�, Vikas Kumar Mishra, Jens Bartsch, Mohammad Al Khatib, Markus Thommes, and Naim Bajcinca. "From Experiment Design to Data-Driven Modeling of Powder Compaction Process." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.101076.

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Tableting is a dry granulation process for compacting powder blends into tablets. In this process, a blend of active pharmaceutical ingredients (APIs) and excipients are fed into the hopper of a rotary tablet press via feeders. Inside the tablet press, rotating feed frame paddle wheels fill powder into dies, with tablet mass adjusted by the lower punch position during the die filling process. Pre-compression rolls press air out of the die, while main compression rolls apply the force necessary for compacting the powder into tablets. In this paper, process variables such as feeder screw speeds,
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Oruganti, Anusha, and G. Ujwala. "Optimizing Hybrid Microgrid Performance with Nonlinear Autoregressive Neural Networks in Grid-Connected and Island Mode Operations." In 2024 2nd International Conference on Advancements and Key Challenges in Green Energy and Computing (AKGEC). IEEE, 2024. https://doi.org/10.1109/akgec62572.2024.10867991.

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Li Xiaoyong, Zhang Zhonghua, Zhu Weikang, Zhou Jinbiao, Chen Guiming, and Yang Lei. "Nonlinear autoregressive model for space tracking ship's swaying data errors." In 2013 2nd International Conference on Measurement, Information and Control (ICMIC). IEEE, 2013. http://dx.doi.org/10.1109/mic.2013.6757981.

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Wu, Ziying, Hongzhao Liu, Lilan Liu, Daning Yuan, and Zhongming Zhang. "Computing of Nonlinear Damping Using the Moving Autoregressive Model Method." In ASME 7th Biennial Conference on Engineering Systems Design and Analysis. ASMEDC, 2004. http://dx.doi.org/10.1115/esda2004-58146.

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Strictly speaking, internal damping of alloy materials is a function of temperature, frequency, strain and strain time rate and so on. Most of the previous papers with regard to damping computing only give a volumetric average when the alloy material is subjected to alternative stress. They cannot accurately describe the natural characteristic of damping. In this paper, the moving autoregressive model method (MARM) is presented to research the relationship between loss factor, strain and frequency of the alloys (Al-33Zn-6Si and Zn-27Al-1Cu). The experimental results show that the loss factor o
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Wibowo, Antoni, Harry Pujianto, and Dewi Retno Sari Saputro. "Nonlinear autoregressive exogenous model (NARX) in stock price index's prediction." In 2017 2nd International Conferences on Information Technology, Information Systems and Electrical Engineering (ICITISEE). IEEE, 2017. http://dx.doi.org/10.1109/icitisee.2017.8285507.

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Zhang, Lei. "Time Series Generation Using Nonlinear Autoregressive Model Artificial Neural Network Based Nonlinear Autoregressive Model Design for the Generation and Prediction of Lorenz Chaotic System." In 2018 IEEE 61st International Midwest Symposium on Circuits and Systems (MWSCAS). IEEE, 2018. http://dx.doi.org/10.1109/mwscas.2018.8623992.

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Ahmed, Adil, and Muhammad Khalid. "A Nonlinear Autoregressive Neural Network Model for Short-Term Wind Forecasting." In 2017 9th IEEE-GCC Conference and Exhibition (GCCCE). IEEE, 2017. http://dx.doi.org/10.1109/ieeegcc.2017.8447983.

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Libal, Urszula, and Karl H. Johansson. "Yule-Walker Equations Using Higher Order Statistics for Nonlinear Autoregressive Model." In 2019 Signal Processing Symposium (SPSympo). IEEE, 2019. http://dx.doi.org/10.1109/sps.2019.8882057.

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Zhang, Xiaoran, Yuting Bai, and Senchun Chai. "State Estimation for GPS Outage Based on Improved Nonlinear Autoregressive Model." In 2018 IEEE 9th International Conference on Software Engineering and Service Science (ICSESS). IEEE, 2018. http://dx.doi.org/10.1109/icsess.2018.8663875.

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