Literatura académica sobre el tema "Nonlinear Autoregressive model"
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Artículos de revistas sobre el tema "Nonlinear Autoregressive model"
Meitz, Mika y Pentti Saikkonen. "PARAMETER ESTIMATION IN NONLINEAR AR–GARCH MODELS". Econometric Theory 27, n.º 6 (31 de mayo de 2011): 1236–78. http://dx.doi.org/10.1017/s0266466611000041.
Texto completoKresnawati, Gayuh, Budi Warsito y Abdul Hoyyi. "PERAMALAN INDEKS HARGA SAHAM GABUNGAN DENGAN METODE LOGISTIC SMOOTH TRANSITION AUTOREGRESSIVE (LSTAR)". Jurnal Gaussian 7, n.º 1 (28 de febrero de 2018): 84–95. http://dx.doi.org/10.14710/j.gauss.v7i1.26638.
Texto completoSheng Lu y Ki H. Chon. "Nonlinear autoregressive and nonlinear autoregressive moving average model parameter estimation by minimizing hypersurface distance". IEEE Transactions on Signal Processing 51, n.º 12 (diciembre de 2003): 3020–26. http://dx.doi.org/10.1109/tsp.2003.818999.
Texto completoBauldry, Shawn y Kenneth A. Bollen. "Nonlinear Autoregressive Latent Trajectory Models". Sociological Methodology 48, n.º 1 (agosto de 2018): 269–302. http://dx.doi.org/10.1177/0081175018789441.
Texto completoSrinivasan, Sundararajan, Tao Ma, Georgios Lazarou y Joseph Picone. "A nonlinear autoregressive model for speaker verification". International Journal of Speech Technology 17, n.º 1 (6 de junio de 2013): 17–25. http://dx.doi.org/10.1007/s10772-013-9201-9.
Texto completoIkoma, Norikazu y Kaoru Hirota. "Nonlinear autoregressive model based on fuzzy relation". Information Sciences 71, n.º 1-2 (junio de 1993): 131–44. http://dx.doi.org/10.1016/0020-0255(93)90068-w.
Texto completoWang, Meiqi, Enli Chen, Pengfei Liu y 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, n.º 1 (enero de 2020): 168781401989650. http://dx.doi.org/10.1177/1687814019896509.
Texto completoXiong, Weili, Wei Fan y 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.
Texto completoSapra, Sunil. "A comparative study of parametric and semiparametric autoregressive models". International Journal of Accounting and Economics Studies 10, n.º 1 (5 de abril de 2022): 15–19. http://dx.doi.org/10.14419/ijaes.v10i1.31978.
Texto completoBlanchard, Tyler y Biswanath Samanta. "Wind speed forecasting using neural networks". Wind Engineering 44, n.º 1 (29 de mayo de 2019): 33–48. http://dx.doi.org/10.1177/0309524x19849846.
Texto completoTesis sobre el tema "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.
Texto completoRech, 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.
Texto completoDiss. 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.
Texto completoDupré, la Tour Tom. "Nonlinear models for neurophysiological time series". Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLT018/document.
Texto completoIn 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.
Texto completoZhou, 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.
Texto completoIn 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.
Texto completo"Change point estimation for threshold autoregressive (TAR) model". 2012. http://library.cuhk.edu.hk/record=b5549066.
Texto completoThis 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 y 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.
Texto completo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 y 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.
Texto completo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.
Libros sobre el tema "Nonlinear Autoregressive model"
Novikov, Anatoliy, Tat'yana Solodkaya, Aleksandr Lazerson y Viktor Polyak. Econometric modeling in the GRETL package. ru: INFRA-M Academic Publishing LLC., 2023. http://dx.doi.org/10.12737/1732940.
Texto completoL, Koul H., ed. Weighted empirical processes in dynamic nonlinear models. 2a ed. New York: Springer, 2002.
Buscar texto completoCapítulos de libros sobre el tema "Nonlinear Autoregressive model"
Castiglione, Juan, Rodrigo Astroza, Saeed Eftekhar Azam y Daniel Linzell. "Output-Only Nonlinear Finite Element Model Updating Using Autoregressive Process". En 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.
Texto completoChhipa, Abrar Ahmed, Vinod Kumar y R. R. Joshi. "Grid-Connected PV System Power Forecasting Using Nonlinear Autoregressive Exogenous Model". En Lecture Notes in Electrical Engineering, 107–24. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-0193-5_10.
Texto completoLe, Tien-Thinh, Binh Thai Pham, Hai-Bang Ly, Ataollah Shirzadi y Lu Minh Le. "Development of 48-hour Precipitation Forecasting Model using Nonlinear Autoregressive Neural Network". En Lecture Notes in Civil Engineering, 1191–96. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0802-8_191.
Texto completoMerzguioui, Mhamed El, Younes Ait Taleb y Mustapha El Jarroudi. "ARCH Model and Nonlinear Autoregressive Neural Networks for Forecasting Financial Time Series". En 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.
Texto completoKoul, Hira L. "Nonlinear Autoregression". En 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.
Texto completoZhang, Lei. "Nonlinear Autoregressive Model Design and Optimization Based on ANN for the Prediction of Chaotic Patterns in EEG Time Series". En Biomedical Engineering and Computational Intelligence, 51–60. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-21726-6_5.
Texto completoKoul, Hira L. "Autoregression". En 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.
Texto completoAdenuga, Olukorede Tijani, Khumbulani Mpofu y Ragosebo Kgaugelo Modise. "Application of ARIMA-LSTM for Manufacturing Decarbonization Using 4IR Concepts". En Lecture Notes in Mechanical Engineering, 115–23. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-18326-3_12.
Texto completoTeräsvirta, Timo. "Nonlinear Models for Autoregressive Conditional Heteroskedasticity". En 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.
Texto completoChodchuangnirun, Benchawanaree, Kongliang Zhu y Woraphon Yamaka. "Pairs Trading via Nonlinear Autoregressive GARCH Models". En Lecture Notes in Computer Science, 276–88. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-75429-1_23.
Texto completoActas de conferencias sobre el tema "Nonlinear Autoregressive model"
Li Xiaoyong, Zhang Zhonghua, Zhu Weikang, Zhou Jinbiao, Chen Guiming y Yang Lei. "Nonlinear autoregressive model for space tracking ship's swaying data errors". En 2013 2nd International Conference on Measurement, Information and Control (ICMIC). IEEE, 2013. http://dx.doi.org/10.1109/mic.2013.6757981.
Texto completoWu, Ziying, Hongzhao Liu, Lilan Liu, Daning Yuan y Zhongming Zhang. "Computing of Nonlinear Damping Using the Moving Autoregressive Model Method". En ASME 7th Biennial Conference on Engineering Systems Design and Analysis. ASMEDC, 2004. http://dx.doi.org/10.1115/esda2004-58146.
Texto completoWibowo, Antoni, Harry Pujianto y Dewi Retno Sari Saputro. "Nonlinear autoregressive exogenous model (NARX) in stock price index's prediction". En 2017 2nd International Conferences on Information Technology, Information Systems and Electrical Engineering (ICITISEE). IEEE, 2017. http://dx.doi.org/10.1109/icitisee.2017.8285507.
Texto completoZhang, 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". En 2018 IEEE 61st International Midwest Symposium on Circuits and Systems (MWSCAS). IEEE, 2018. http://dx.doi.org/10.1109/mwscas.2018.8623992.
Texto completoAhmed, Adil y Muhammad Khalid. "A Nonlinear Autoregressive Neural Network Model for Short-Term Wind Forecasting". En 2017 9th IEEE-GCC Conference and Exhibition (GCCCE). IEEE, 2017. http://dx.doi.org/10.1109/ieeegcc.2017.8447983.
Texto completoLibal, Urszula y Karl H. Johansson. "Yule-Walker Equations Using Higher Order Statistics for Nonlinear Autoregressive Model". En 2019 Signal Processing Symposium (SPSympo). IEEE, 2019. http://dx.doi.org/10.1109/sps.2019.8882057.
Texto completoZhang, Xiaoran, Yuting Bai y Senchun Chai. "State Estimation for GPS Outage Based on Improved Nonlinear Autoregressive Model". En 2018 IEEE 9th International Conference on Software Engineering and Service Science (ICSESS). IEEE, 2018. http://dx.doi.org/10.1109/icsess.2018.8663875.
Texto completoHamada, Ayaka, Harushi Nagatsuma, Shoko Oikawa y Toshiya Hirose. "Constructing Model of Bicycle Behavior on Non-signalized lntersection Using Nonlinear Autoregressive Exogenous Model". En International Cycling Safety Conference. Technische Universität Dresden, 2022. http://dx.doi.org/10.25368/2022.423.
Texto completoChuanjin Jiang y Fugen Song. "Forecasting chaotic time series of exchange rate based on nonlinear autoregressive model". En 2010 2nd International Conference on Advanced Computer Control. IEEE, 2010. http://dx.doi.org/10.1109/icacc.2010.5487266.
Texto completoMiyata, Akihiro, Masato Gokan y Toshiya Hirose. "Accuracy of a Driver Model with Nonlinear AutoregRessive with eXogeous Inputs (NARX)". En 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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