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Автор: Grafiati

Опубліковано: 14 березня 2023

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Статті в журналах з теми "Nonlinear Autoregressive model"

Meitz, Mika, and Pentti Saikkonen. "PARAMETER ESTIMATION IN NONLINEAR AR–GARCH MODELS." Econometric Theory 27, no. 6 (May 31, 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 established under conditions comparable to those recently used in the corresponding linear case. To the best of our knowledge, this paper provides the first results on consistency and asymptotic normality of the QML estimator in nonlinear autoregressive models with GARCH errors.

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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 (February 28, 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 IHSG data for the next 15 period has Mean Absolute Percentage Error (MAPE) 2,932612%. Keywords : autoregressive, LSTAR, nonlinier, time series

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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 (December 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 (August 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 on two forms of nonlinear autoregressive latent trajectory (NLALT) models. The first form allows for a quadratic growth trajectory, a popular form of nonlinear latent growth curve models. The second form derives from latent basis models, or freed loading models, that allow for arbitrary growth processes. We discuss details concerning parameterization, model identification, estimation, and testing for the two forms of NLALT models. We include a simulation study that illustrates potential biases that may arise from fitting alternative models to data derived from an autoregressive process and individual-specific nonlinear trajectories. In addition, we include an extended empirical example modeling growth trajectories of weight from birth through age 2.

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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 (June 6, 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 (June 1993): 131–44. http://dx.doi.org/10.1016/0020-0255(93)90068-w.

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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 (January 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 characteristics of the sintering system. By obtaining the system gains based on the steady-state model, the parameters of the dynamic model are rectified online to conform to the system gain. Thus, a dynamic model named extreme learning machine-autoregressive exogenous is proposed, which can describe the nonlinear dynamic features of a sintering system. The results show that, compared with the autoregressive exogenous model, the extreme learning machine-autoregressive exogenous model has good control performance on the multivariable and nonlinear system and can reduce computing resource requirements during the online running. In addition, fluctuations of NOx and O2 concentrations decreases, again demonstrating good control performance of an actual clinker sintering system using the extreme learning machine-autoregressive exogenous model.

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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 provided.

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Sapra, Sunil. "A comparative study of parametric and semiparametric autoregressive models." International Journal of Accounting and Economics Studies 10, no. 1 (April 5, 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 autoregressive multivariate adaptive regression splines (MARS-AR) studied in the paper automatically discover and incorporate nonlinearities in autoregressive (AR) models.Â Performance comparisons among these semiparametric AR models and the linear AR model are carried out via their application to Australian data on growth in GDP and unemployment using RMSE and GCV measures.Â Â

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Blanchard, Tyler, and Biswanath Samanta. "Wind speed forecasting using neural networks." Wind Engineering 44, no. 1 (May 29, 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 both models were suitable for wind speed prediction. Both models outperformed persistence model (with a factor of about 2 to 10 in root mean square error ratio). Both artificial neural network models were implemented for single-step and multi-step-ahead prediction of wind speed for all four locations and results were compared. Nonlinear autoregressive neural network with exogenous inputs model gave better prediction performance than nonlinear autoregressive model and the difference was statistically significant.

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Дисертації з теми "Nonlinear Autoregressive model"

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 production index. Estimation results imply that capacity utilization rate and growth of industrial production index show M-TAR type nonlinear stationary behavior according to the unit root test proposed by Enders and Granger (1998).

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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 lowest value of the model selection criterion is selected. The second essay, Modelling and Forecasting Economic Time Series with Single Hidden-layer Feedforward Autoregressive Artificial Neural Networks, proposes an unified framework for artificial neural network modelling. Linearity is tested and the selection of regressors performed by the methodology developed in essay I. The number of hidden units is detected by a procedure based on a sequence of Lagrange multiplier (LM) tests. Serial correlation of errors and parameter constancy are checked by LM tests as well. A Monte-Carlo study, the two classical series of the lynx and the sunspots, and an application on the monthly S&P 500 index return series are used to demonstrate the performance of the overall procedure. In the third essay, Forecasting with Artificial Neural Network Models (in cooperation with Marcelo Medeiros), the methodology developed in essay II, the most popular methods for artificial neural network estimation, and the linear autoregressive model are compared by forecasting performance on 30 time series from different subject areas. Early stopping, pruning, information criterion pruning, cross-validation pruning, weight decay, and Bayesian regularization are considered. The findings are that 1) the linear models very often outperform the neural network ones and 2) the modelling approach to neural networks developed in this thesis stands up well with in comparison when compared to the other neural network modelling methods considered here.

Diss. Stockholm : Handelshögskolan, 2002. Spikblad saknas

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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 to thrive with regards to attaining a specific critical population number, which results in maximising the biogas production. This problem continues to exist as a result of insufficient knowledge of the interactions between the operating parameters and bacterial community. In addition, the lack of sufficient knowledge of the composition of bacterial groups that varies with changes in the operating parameters such as temperature, substrate and retention time. Without sufficient knowledge of the overall impact of the physico-environmental operating parameters on anaerobic bacterial growth and composition, significant improvement of biogas production may be difficult to attain. In order to mitigate this problem, this study has presented a nonlinear multi-parameter system modelling of mesophilic AD. It utilised raw data sets generated from laboratory experimentation of the influence of four operating parameters, temperature, pH, mixing speed and pressure on biogas and methane production, signifying that this is a multiple input single output (MISO) system. Due to the nonlinear characteristics of the data, the nonlinear black-box modelling technique is applied. The modelling is performed in MATLAB through System Identification approach. Two nonlinear model structures, autoregressive with exogenous input (NARX) and Hammerstein-Wiener (NLHW) with different nonlinearity estimators and model orders are chosen by trial and error and utilised to estimate the models. The performance of the models is determined by comparing the simulated outputs of the estimated models and the output in the validation data. The approach is used to validate the estimated models by checking how well the simulated output of the models fits the measured output. The best models for biogas and methane production are chosen by comparing the outputs of the best NARX and NLHW models (each for biogas and methane production), and the validation data, as well as utilising the Akaike information criterion to measure the quality of each model relative to each of the other models. The NLHW models mhw2 and mhws2 are chosen for biogas and methane production, respectively. The identified NLHW models mhw2 and mhws2 represent the behaviour of the production of biogas and methane, respectively, from mesophilic AD. Among all the candidate models studied, the nonlinear models provide a superior reproduction of the experimental data over the whole analysed period. Furthermore, the models constructed in this study cannot be used for scale-up purpose because they are not able to satisfy the rules and criteria for applying dimensional analysis to scale-up.

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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 modélisation permet une sélection de modèle efficace grâce à la mesure de vraisemblance, ce qui constitue un apport majeur à l'estimation du PAC.%Nous présentons différentes paramétrisations des modèles DAR et leurs algorithmes d'inférence rapides, et discutons de leur stabilité.Puis nous montrons comment utiliser les modèles DAR pour l'analyse du PAC, et démontrons l'avantage de l'approche par modélisation avec trois jeux de donnée.Puis nous explorons plusieurs extensions à ces modèles, pour estimer le signal pilote à partir des données, le PAC sur des signaux multivariés, ou encore des champs réceptifs spectro-temporels.Enfin, nous proposons aussi d'adapter les modèles de codage parcimonieux convolutionnels pour les séries temporelles neurophysiologiques, en les étendant à des distributions à queues lourdes et à des décompositions multivariées. Nous développons des algorithmes d'inférence efficaces pour chaque formulations, et montrons que l'on obtient de riches représentations de façon non-supervisée
In neurophysiological time series, strong neural oscillations are observed in the mammalian brain, and the natural processing tools are thus centered on narrow-band linear filtering.As this approach is too reductive, we propose new methods to represent these signals.We first focus on the study of phase-amplitude coupling (PAC), which consists in an amplitude modulation of a high frequency band, time-locked with a specific phase of a slow neural oscillation.We propose to use driven autoregressive models (DAR), to capture PAC in a probabilistic model. Giving a proper model to the signal enables model selection by using the likelihood of the model, which constitutes a major improvement in PAC estimation.%We first present different parametrization of DAR models, with fast inference algorithms and stability discussions.Then, we present how to use DAR models for PAC analysis, demonstrating the advantage of the model-based approach on three empirical datasets.Then, we explore different extensions to DAR models, estimating the driving signal from the data, PAC in multivariate signals, or spectro-temporal receptive fields.Finally, we also propose to adapt convolutional sparse coding (CSC) models for neurophysiological time-series, extending them to heavy-tail noise distribution and multivariate decompositions. We develop efficient inference algorithms for each formulation, and show that we obtain rich unsupervised signal representations

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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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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 model and the ESTAR model. The results from the estimated model suggest the STAR model is better performing than the linear autoregressive model.

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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 the new models are presented, as it is important in order to see what characteristics real data which could be explained by them should have. In addition, the models are applied to various stationary and nonstationary economic and financial time series and are compared to the classic AR-ARCH and AR-GARCH models, in terms of fitting and forecasting. It is shown that, although it is difficult to beat the AR-ARCH and AR-GARCH models, the ExpAR and Extended ExpAR models and their special cases, combined with conditional heteroscedastic errors, can be useful tools in fitting, describing and forecasting nonlinear behaviour in financial and economic time series, and can provide some improvement in terms of both fitting and forecasting compared to the AR-ARCH and AR-GARCH models.

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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。
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 optimization problem and the minimum description length principle is applied to compare various segmented TAR. We also show the consistency of the minimal MDL model selection procedure under general regularity conditions on the likelihood function.
Detailed summary in vernacular field only.
Tang, Chong Man.
Thesis (M.Phil.)--Chinese University of Hong Kong, 2012.
Includes bibliographical references (leaves 45-47).
Abstracts also in Chinese.
Chapter 1 --- Introduction --- p.1
Chapter 1.1 --- Introduction --- p.1
Chapter 2 --- Minimum Description Length for Pure TAR --- p.4
Chapter 2.1 --- Model selection using Minimum Description Length for Pure TAR --- p.4
Chapter 2.1.1 --- Derivation of Minimum Description Length for Pure TAR --- p.5
Chapter 2.2 --- Optimization Using Genetic Algorithms (GA) --- p.7
Chapter 2.2.1 --- General Description --- p.7
Chapter 2.2.2 --- Implementation Details --- p.9
Chapter 3 --- Minimum Description Length for TAR models with structural change --- p.13
Chapter 3.1 --- Model selection using Minimum Description Length for TAR models with structural change --- p.13
Chapter 3.1.1 --- Derivation of Minimum Description Length for TAR models with structural change --- p.14
Chapter 3.2 --- Optimization Using Genetic Algorithms --- p.17
Chapter 4 --- Main Result --- p.20
Chapter 4.1 --- Main results --- p.20
Chapter 4.1.1 --- Model Selection using minimum description length --- p.21
Chapter 5 --- Simulation Result --- p.24
Chapter 5.1 --- Simulation results --- p.24
Chapter 5.1.1 --- Example of TAR Model Without Structural Break --- p.24
Chapter 5.1.2 --- Example of TAR Model With Structural Break I --- p.26
Chapter 5.1.3 --- Example of TAR Model With Structural Break II --- p.29
Chapter 6 --- An empirical example --- p.33
Chapter 6.1 --- An empirical example --- p.33
Chapter 7 --- Consistency of the CLSE --- p.36
Chapter 7.1 --- Consistency of the TAR parameters --- p.36
Chapter 7.1.1 --- Consistency of the estimation of number of threshold --- p.36
Chapter 7.1.2 --- Consistency of the change point parameters --- p.43
Bibliography --- p.45

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Lin, Gang-Yi, and 林罡亦. "Application of Nonlinear Autoregressive with Exogenous Input Model to Estimate the Linear Modal Parameters of Nonlinear Systems." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/76748314142063084574.

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碩士
國立臺灣大學
工程科學及海洋工程學研究所
97
Since the real mechanical systems have nonlinear factors, the only differences are the extent of nonlinearity, so the vibration phenomenon actually are nonlinear. Since the real system has damping, so the oscillation frequency of non-linear system change with amplitude. Thus it’s difficult to estimate the oscillation frequency of a non-linear systems. However, the natural frequency of any system is natural and is not influenced by other factors. This article purposes a set of identification process to estimate the linear modal parameters of nonlinear systems. At first in this thesis, it is to simulate the output response on both a single and three degrees of freedom of the non-linear systems with damping by using numerical simulation. We can compute the output response of a nonlinear vibration system using system identification techniques by the mathematical model of Nonlinear AutoRegressive with eXogenous inputs model combined with Volterra series to estimate the linear modal parameters of nonlinear systems. Besides, in the analytic process, it also utilizes power spectral density diagram, time frequency analysis diagram and modal stabilization diagram to assist the reach. Finally, NARX method is applied to the two experimental examples, cantilever beam and framed structure of motorcycle. cantilever beam used to test the free response of the system identification information. Framed structure of motorcycle were excitation by hammer and shaker to discuss the identification ability of NARX method under some noise disturbance. By comparing the numerical and the experimental data, for system identificationtechnique involved can work well to estimate the linear modal parameters of nonlinear systems

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Shiu-TongJain and 簡旭彤. "Nonlinear Autoregressive Exogenous Model for Wind Power Forecasting and Wind Turbine Health Monitoring." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/djfnc8.

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碩士
國立成功大學
航空太空工程學系
104
In the recent years, renewable energy with zero pollution has been emphasized by many countries. Wind energy is wildly used due to its clean and renewable properties. Forecasting the output power of the wind turbine generators is a highly focus topic now. It’s important to the power company and the wind power company of predicting the wind energy precisely, which they applied to reduce cost and raise the quality. However, due to the randomness and the instability characteristics, it’s a great challenge to predict wind power accurately. Moreover, monitoring wind turbine health is also important. As long as an error is detected, it can be fixed right away. There are a lots of research that built plenty of mathematical models to predict wind power. An input-output property forecasting mathematical model is established to complete the forecasting and wind turbine health monitoring by using actual data recorded from the real wind turbines. By seeking out the time delay from the coherences between wind speed and output power, the accuracy can be improved by combining with autoregressive approach. By using the MANOVA of the multivariate analysis and applications to analysis the parameters of the model. The status of the wind turbine can be detected by finding the correlations between parameters to reach the goal of monitoring the health of the wind turbine.

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Книги з теми "Nonlinear Autoregressive model"

Novikov, Anatoliy, Tat'yana Solodkaya, Aleksandr Lazerson, and Viktor Polyak. Econometric modeling in the GRETL package. ru: 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. For the convenience of users, the tutorial contains all the task data files used in the work in the format .GDTs are collected in an application in the cloud so that users have access to them. Meets the requirements of the federal state educational standards of higher education of the latest generation in the disciplines of "Econometrics" and "Econometric modeling". For students and teachers of economic universities in the field of Economics, as well as researchers who use econometric methods to model socio-economic phenomena and processes.

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L, Koul H., ed. Weighted empirical processes in dynamic nonlinear models. 2nd ed. New York: Springer, 2002.

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Частини книг з теми "Nonlinear Autoregressive model"

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, 83–86. Cham: 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, 107–24. Singapore: 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, 1191–96. Singapore: 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, 484–98. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-26852-6_45.

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Koul, Hira L. "Nonlinear Autoregression." In Weighted Empirical Processes in Dynamic Nonlinear Models, 358–407. New York, NY: Springer New York, 2002. http://dx.doi.org/10.1007/978-1-4613-0055-7_8.

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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, 51–60. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-21726-6_5.

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Koul, Hira L. "Autoregression." In Weighted Empirical Processes in Dynamic Nonlinear Models, 294–357. New York, NY: Springer New York, 2002. http://dx.doi.org/10.1007/978-1-4613-0055-7_7.

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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, 115–23. Cham: 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-term memory network (LSTM) model for energy consumption forecasting and prediction of carbon emission within the manufacturing facility using the 4IR concept. The method could capture linear features (ARIMA) and LSTM captures the long dependencies in the data from the nonlinear time series data patterns, Root means square error (RMSE) is used for data analysis comparing the performance of ARIMA which is 448.89 as a single model with ARIMA-LSTM hybrid model as actual (trained) and predicted (test) 59.52 and 58.41 respectively. The results depicted RMSE values of ARIMA-LSTM being extremely smaller than ARIMA, which proves that hybrid ARIMA-LSTM is more suitable for prediction than ARIMA.

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Teräsvirta, Timo. "Nonlinear Models for Autoregressive Conditional Heteroskedasticity." In Handbook of Volatility Models and Their Applications, 47–69. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781118272039.ch2.

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Chodchuangnirun, Benchawanaree, Kongliang Zhu, and Woraphon Yamaka. "Pairs Trading via Nonlinear Autoregressive GARCH Models." In Lecture Notes in Computer Science, 276–88. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-75429-1_23.

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Тези доповідей конференцій з теми "Nonlinear Autoregressive model"

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 of alloy increases with the increasing strain, and increases with the increasing frequency in low-frequency region (below 400Hz). The damping appears strong nonlinear behavior. The three-dimension graph of the loss factor versus strain and frequency provides useful information for the optimum design of machine parts made from damping alloy.

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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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Hamada, Ayaka, Harushi Nagatsuma, Shoko Oikawa, and Toshiya Hirose. "Constructing Model of Bicycle Behavior on Non-signalized lntersection Using Nonlinear Autoregressive Exogenous Model." In International Cycling Safety Conference. Technische Universität Dresden, 2022. http://dx.doi.org/10.25368/2022.423.

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Chuanjin Jiang and Fugen Song. "Forecasting chaotic time series of exchange rate based on nonlinear autoregressive model." In 2010 2nd International Conference on Advanced Computer Control. IEEE, 2010. http://dx.doi.org/10.1109/icacc.2010.5487266.

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Miyata, Akihiro, Masato Gokan, and Toshiya Hirose. "Accuracy of a Driver Model with Nonlinear AutoregRessive with eXogeous Inputs (NARX)." In WCX World Congress Experience. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2018. http://dx.doi.org/10.4271/2018-01-0504.

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