自己回帰モデル
(自己回帰 から転送)
出典: フリー百科事典『ウィキペディア(Wikipedia)』 (2024/06/02 08:42 UTC 版)
自己回帰モデル(じこかいきモデル、英: autoregressive model)は時点 t におけるモデル出力が時点 t 以前のモデル出力に依存する確率過程である。ARモデルとも呼ばれる。
- ^ Zetterberg, Lars H. (1969), “Estimation of parameters for a linear difference equation with application to EEG analysis”, Mathematical Biosciences 5 (3): 227--275, doi:10.1016/0025-5564(69)90044-3, ISSN 0025-5564
- ^ Yule, G. Udny (1927), “On a Method of Investigating Periodicities in Disturbed Series, with Special Reference to Wolfer's Sunspot Numbers”, Philosophical Transactions of the Royal Society of London, Ser. A 226: 267–298
- ^ Walker, Gilbert (1931), “On Periodicity in Series of Related Terms”, Proceedings of the Royal Society of London, Ser. A 131: 518–532
- ^ a b Hamilton & (1994), p. 59
- ^ a b Von Storch, H.; F. W Zwiers (2001). Statistical analysis in climate research. Cambridge Univ Pr. ISBN 0-521-01230-9[要ページ番号]
- ^ Hamilton & (1994), Chapter 3 and 5
- ^ Burg, John P. (1968), “A new analysis technique for time series data”, in D. G. Childers, Modern Spectrum Analysis, NATO Advanced Study Institute of Signal Processing with emphasis on Underwater Acoustics, New York: IEEE Press
- ^ Brockwell, Peter J.; Dahlhaus, Rainer; Trindade, A. Alexandre (2005). “Modified Burg Algorithms for Multivariate Subset Autoregression”. Statistica Sinica 15: 197–213 .
- ^ Burg, John P. (1967), “Maximum Entropy Spectral Analysis”, Proceedings of the 37th Meeting of the Society of Exploration Geophysicists (Oklahoma: Oklahoma City)
- ^ Bos, R.; De Waele, S.; Broersen, P. M. T. (2002). “Autoregressive spectral estimation by application of the burg algorithm to irregularly sampled data”. IEEE Transactions on Instrumentation and Measurement 51 (6): 1289. doi:10.1109/TIM.2002.808031.
- ^ Hamilton & (1994), p. 155
- ^ "Fit Autoregressive Models to Time Series" (in R)
- ^ Econometrics Toolbox Overview
- ^ System Identification Toolbox overview
- ^ "Autoregressive modeling in MATLAB"
- ^ "Time Series Analysis toolbox for Matlab and Octave"
- ^ a b 亀岡 (2019) 深層生成モデルを用いた音声音響信号処理. http://www.kecl.ntt.co.jp/people/kameoka.hirokazu/publications/Kameoka2019SICE03_published.pdf
- ^ The model is fully probabilistic and autoregressive, with the predictive distribution for each audio sample conditioned on all previous ones... Aaron van den Oord, et al.. (2016) WaveNet: A Generative Model for Raw Audio
- ^ "to replace the actual output of a unit by the teacher signal in subsequent computation of the behavior of the network, whenever such a value exists. We call this technique 'teacher forcing.' " Williams & Zipser. (1989). A Learning Algorithm for Continually Running Fully Recurrent Neural Networks. doi: 10.1162/neco.1989.1.2.270
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