Littérature scientifique sur le sujet « Nonlinear Autoregressive model »
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Articles de revues sur le sujet "Nonlinear Autoregressive model"
Meitz, Mika, et Pentti Saikkonen. « PARAMETER ESTIMATION IN NONLINEAR AR–GARCH MODELS ». Econometric Theory 27, no 6 (31 mai 2011) : 1236–78. http://dx.doi.org/10.1017/s0266466611000041.
Texte intégralKresnawati, Gayuh, Budi Warsito et Abdul Hoyyi. « PERAMALAN INDEKS HARGA SAHAM GABUNGAN DENGAN METODE LOGISTIC SMOOTH TRANSITION AUTOREGRESSIVE (LSTAR) ». Jurnal Gaussian 7, no 1 (28 février 2018) : 84–95. http://dx.doi.org/10.14710/j.gauss.v7i1.26638.
Texte intégralSheng Lu et 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 (décembre 2003) : 3020–26. http://dx.doi.org/10.1109/tsp.2003.818999.
Texte intégralBauldry, Shawn, et Kenneth A. Bollen. « Nonlinear Autoregressive Latent Trajectory Models ». Sociological Methodology 48, no 1 (août 2018) : 269–302. http://dx.doi.org/10.1177/0081175018789441.
Texte intégralSrinivasan, Sundararajan, Tao Ma, Georgios Lazarou et Joseph Picone. « A nonlinear autoregressive model for speaker verification ». International Journal of Speech Technology 17, no 1 (6 juin 2013) : 17–25. http://dx.doi.org/10.1007/s10772-013-9201-9.
Texte intégralIkoma, Norikazu, et Kaoru Hirota. « Nonlinear autoregressive model based on fuzzy relation ». Information Sciences 71, no 1-2 (juin 1993) : 131–44. http://dx.doi.org/10.1016/0020-0255(93)90068-w.
Texte intégralWang, Meiqi, Enli Chen, Pengfei Liu et 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 (janvier 2020) : 168781401989650. http://dx.doi.org/10.1177/1687814019896509.
Texte intégralXiong, Weili, Wei Fan et 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.
Texte intégralSapra, Sunil. « A comparative study of parametric and semiparametric autoregressive models ». International Journal of Accounting and Economics Studies 10, no 1 (5 avril 2022) : 15–19. http://dx.doi.org/10.14419/ijaes.v10i1.31978.
Texte intégralBlanchard, Tyler, et Biswanath Samanta. « Wind speed forecasting using neural networks ». Wind Engineering 44, no 1 (29 mai 2019) : 33–48. http://dx.doi.org/10.1177/0309524x19849846.
Texte intégralThèses sur le sujet "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.
Texte intégralRech, 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.
Texte intégralDiss. Stockholm : Handelshögskolan, 2002. Spikblad saknas
Ogbonna, Emmanuel. « A multi-parameter empirical model for mesophilic anaerobic digestion ». Thesis, University of Hertfordshire, 2017. http://hdl.handle.net/2299/17467.
Texte intégralDupré, la Tour Tom. « Nonlinear models for neurophysiological time series ». Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLT018/document.
Texte intégralIn 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
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.
Texte intégralZhou, 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.
Texte intégralIn 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.
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.
Texte intégral« Change point estimation for threshold autoregressive (TAR) model ». 2012. http://library.cuhk.edu.hk/record=b5549066.
Texte intégralThis 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
Lin, Gang-Yi, et 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.
Texte intégral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
Shiu-TongJain et 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.
Texte intégral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.
Livres sur le sujet "Nonlinear Autoregressive model"
Novikov, Anatoliy, Tat'yana Solodkaya, Aleksandr Lazerson et Viktor Polyak. Econometric modeling in the GRETL package. ru : INFRA-M Academic Publishing LLC., 2023. http://dx.doi.org/10.12737/1732940.
Texte intégralL, Koul H., dir. Weighted empirical processes in dynamic nonlinear models. 2e éd. New York : Springer, 2002.
Trouver le texte intégralChapitres de livres sur le sujet "Nonlinear Autoregressive model"
Castiglione, Juan, Rodrigo Astroza, Saeed Eftekhar Azam et Daniel Linzell. « Output-Only Nonlinear Finite Element Model Updating Using Autoregressive Process ». Dans 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.
Texte intégralChhipa, Abrar Ahmed, Vinod Kumar et R. R. Joshi. « Grid-Connected PV System Power Forecasting Using Nonlinear Autoregressive Exogenous Model ». Dans Lecture Notes in Electrical Engineering, 107–24. Singapore : Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-0193-5_10.
Texte intégralLe, Tien-Thinh, Binh Thai Pham, Hai-Bang Ly, Ataollah Shirzadi et Lu Minh Le. « Development of 48-hour Precipitation Forecasting Model using Nonlinear Autoregressive Neural Network ». Dans Lecture Notes in Civil Engineering, 1191–96. Singapore : Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0802-8_191.
Texte intégralMerzguioui, Mhamed El, Younes Ait Taleb et Mustapha El Jarroudi. « ARCH Model and Nonlinear Autoregressive Neural Networks for Forecasting Financial Time Series ». Dans 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.
Texte intégralKoul, Hira L. « Nonlinear Autoregression ». Dans 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.
Texte intégralZhang, Lei. « Nonlinear Autoregressive Model Design and Optimization Based on ANN for the Prediction of Chaotic Patterns in EEG Time Series ». Dans Biomedical Engineering and Computational Intelligence, 51–60. Cham : Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-21726-6_5.
Texte intégralKoul, Hira L. « Autoregression ». Dans 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.
Texte intégralAdenuga, Olukorede Tijani, Khumbulani Mpofu et Ragosebo Kgaugelo Modise. « Application of ARIMA-LSTM for Manufacturing Decarbonization Using 4IR Concepts ». Dans Lecture Notes in Mechanical Engineering, 115–23. Cham : Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-18326-3_12.
Texte intégralTeräsvirta, Timo. « Nonlinear Models for Autoregressive Conditional Heteroskedasticity ». Dans 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.
Texte intégralChodchuangnirun, Benchawanaree, Kongliang Zhu et Woraphon Yamaka. « Pairs Trading via Nonlinear Autoregressive GARCH Models ». Dans Lecture Notes in Computer Science, 276–88. Cham : Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-75429-1_23.
Texte intégralActes de conférences sur le sujet "Nonlinear Autoregressive model"
Li Xiaoyong, Zhang Zhonghua, Zhu Weikang, Zhou Jinbiao, Chen Guiming et Yang Lei. « Nonlinear autoregressive model for space tracking ship's swaying data errors ». Dans 2013 2nd International Conference on Measurement, Information and Control (ICMIC). IEEE, 2013. http://dx.doi.org/10.1109/mic.2013.6757981.
Texte intégralWu, Ziying, Hongzhao Liu, Lilan Liu, Daning Yuan et Zhongming Zhang. « Computing of Nonlinear Damping Using the Moving Autoregressive Model Method ». Dans ASME 7th Biennial Conference on Engineering Systems Design and Analysis. ASMEDC, 2004. http://dx.doi.org/10.1115/esda2004-58146.
Texte intégralWibowo, Antoni, Harry Pujianto et Dewi Retno Sari Saputro. « Nonlinear autoregressive exogenous model (NARX) in stock price index's prediction ». Dans 2017 2nd International Conferences on Information Technology, Information Systems and Electrical Engineering (ICITISEE). IEEE, 2017. http://dx.doi.org/10.1109/icitisee.2017.8285507.
Texte intégralZhang, 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 ». Dans 2018 IEEE 61st International Midwest Symposium on Circuits and Systems (MWSCAS). IEEE, 2018. http://dx.doi.org/10.1109/mwscas.2018.8623992.
Texte intégralAhmed, Adil, et Muhammad Khalid. « A Nonlinear Autoregressive Neural Network Model for Short-Term Wind Forecasting ». Dans 2017 9th IEEE-GCC Conference and Exhibition (GCCCE). IEEE, 2017. http://dx.doi.org/10.1109/ieeegcc.2017.8447983.
Texte intégralLibal, Urszula, et Karl H. Johansson. « Yule-Walker Equations Using Higher Order Statistics for Nonlinear Autoregressive Model ». Dans 2019 Signal Processing Symposium (SPSympo). IEEE, 2019. http://dx.doi.org/10.1109/sps.2019.8882057.
Texte intégralZhang, Xiaoran, Yuting Bai et Senchun Chai. « State Estimation for GPS Outage Based on Improved Nonlinear Autoregressive Model ». Dans 2018 IEEE 9th International Conference on Software Engineering and Service Science (ICSESS). IEEE, 2018. http://dx.doi.org/10.1109/icsess.2018.8663875.
Texte intégralHamada, Ayaka, Harushi Nagatsuma, Shoko Oikawa et Toshiya Hirose. « Constructing Model of Bicycle Behavior on Non-signalized lntersection Using Nonlinear Autoregressive Exogenous Model ». Dans International Cycling Safety Conference. Technische Universität Dresden, 2022. http://dx.doi.org/10.25368/2022.423.
Texte intégralChuanjin Jiang et Fugen Song. « Forecasting chaotic time series of exchange rate based on nonlinear autoregressive model ». Dans 2010 2nd International Conference on Advanced Computer Control. IEEE, 2010. http://dx.doi.org/10.1109/icacc.2010.5487266.
Texte intégralMiyata, Akihiro, Masato Gokan et Toshiya Hirose. « Accuracy of a Driver Model with Nonlinear AutoregRessive with eXogeous Inputs (NARX) ». Dans WCX World Congress Experience. 400 Commonwealth Drive, Warrendale, PA, United States : SAE International, 2018. http://dx.doi.org/10.4271/2018-01-0504.
Texte intégral