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Dynamic and sequential update for time series forecasting

H J Gallardo Pérez, M Vergel Ortega, J P Rojas-Suárez
2020 Journal of Physics, Conference Series  
The main objective of this research is to adjust a model, using the methodology framed in the sequential update procedure of the forecast, to a time series of coal production observed quarterly during  ...  Once the process has been carried out and validated, a quarterly production model is estimated which allows valid and reliable forecasts to be made for each quarter in subsequent years.  ...  The proposed model is constructed from two points of view: intra-and inter-stations, obtaining a composite model ARIMA(p,d,q)x(P,D,Q)s in which p and q represent the orders of the autoregressive and moving  ... 
doi:10.1088/1742-6596/1587/1/012016 fatcat:fzzjfkz6hresnlxwu72x2i7cly

Time domain methods for X-ray and gamma-ray astronomy [article]

Eric D. Feigelson, Vinay L. Kashyap, Aneta Siemiginowska
2022 arXiv   pre-print
autoregressive models, C-statistic and Poisson regression) methods.  ...  Once detected, variability can be characterized by nonparametric (autocorrelation function, structure function,wavelet analysis) and parametric (multiple change point model such as Bayesian Blocks, integer  ...  In such cases, local regression, wavelet analysis [Mallat 2008 , Nason 2008 ] and multiple change point analysis [Tartakovsky et al. 2020 ] might be effective analysis methods.  ... 
arXiv:2203.08996v1 fatcat:arfcliduv5ccdlhj24yfexk3na

Parameterization Methods and Autoregressive Model [chapter]

Boukari Nassim
2019 Smart Healthcare [Working Title]  
The first phase for the treatment of random signals is the feature extraction; in this we can find several methods for that.  ...  In this chapter, we presented the autoregressive (AR) method, some methods of univariate and multivariate measures, and examples of their applications.  ...  So, it should be detected early, for which several techniques are in use such as traditional techniques based on Fourier analysis, but the most common techniques use an autoregressive (AR) model.  ... 
doi:10.5772/intechopen.86038 fatcat:ag5m2mlc3vfsxazn2c54istski

Classification of Motor Imagery Using Combination of Feature Extraction and Reduction Methods for Brain-Computer Interface

Vacius Jusas, Sam Gilvine Samuvel
2019 Information Technology and Control  
The research presented in the paper explores the methods of band power, time domain parameters, fast Fourier transform and channel variance for feature extraction.  ...  The combination of the methods of fast Fourier transform, channel variance and principal component analysis performed the best among the combinations of methods.  ...  [25] combined autoregressive model and sample entropy for the feature extraction.  ... 
doi:10.5755/j01.itc.48.2.23091 fatcat:e4zzqljpmje6dfszbq3exsxerm


Sergey Goolak, Viktor Tkachenko, Gintautas Bureika, Gediminas Vaičiūnas
2021 Transport  
Traditionally, spectral analysis in such systems is performed using Fourier methods.  ...  Thus, in the case under consideration, the application of the Fourier methods is incorrect for the analysis of the spectral components of the traction current.  ...  If the form of voltages u and currents i is non-sinusoidal, it is advisable to use classical Fourier analysis for spectral analysis of processes in power systems.  ... 
doi:10.3846/transport.2020.14242 fatcat:bl23agzuvnal3bjlj2rpd3vnzi

A methodological comparison of the Porges algorithm, fast Fourier transform, and autoregressive spectral analysis for the estimation of heart rate variability in 5-month-old infants

Natalia Poliakova, Ginette Dionne, Etienne Dubreuil, Blaine Ditto, Robert O. Pihl, Daniel Pérusse, Richard E. Tremblay, Michel Boivin
2014 Psychophysiology  
The aim was to compare three methods of HRV estimation: 1) fast Fourier transform (FFT), 2) autoregressive (AR) and 3) the Porges methods. HRV was estimated in 63 healthy 5-month-old infants.  ...  Little empirical evidence exists on the comparability of heart rate variability (HRV) quantification methods commonly used in infants.  ...  We thank Jocelyn Malo for coordinating the data collection, and Hélène Paradis, Bernadette Simoneau and Jacqueline Langlois for their assistance in data management.  ... 
doi:10.1111/psyp.12194 pmid:24611569 fatcat:fpeyu2vaofhftiajc5gae7wmju

Spectral estimation-What is new? What is next?

Jean Baptiste Tary, Roberto Henry Herrera, Jiajun Han, Mirko van der Baan
2014 Reviews of Geophysics  
Methods Short-Time Fourier Transform The Fourier transform is a measure of the similarity of a signal with a family basis formed by sines and cosines.  ...  Spectral estimation, and corresponding time-frequency representation for nonstationary signals, is a cornerstone in geophysical signal processing and interpretation.  ...  Acknowledgments The authors would like to thank the sponsors of the Microseismic Industry consortium for financial support, and  ... 
doi:10.1002/2014rg000461 fatcat:6sm3l7wwbjb47au37oonf4sxim

Detecting alpha spindle events in EEG time series using adaptive autoregressive models

Vernon Lawhern, Scott Kerick, Kay A Robbins
2013 BMC Neuroscience  
Methods: In this work we propose modeling the alpha band EEG time series using discounted autoregressive (DAR) modeling.  ...  Conclusion: Modeling the alpha band EEG using discounted AR models provides an efficient method for detecting oscillatory alpha activity in EEG.  ...  David Hairston and Kaleb McDowell, both with the Army Research Laboratory, for helpful discussions, and DCS Corporation for the support and development of the experimental design used in this study.  ... 
doi:10.1186/1471-2202-14-101 pmid:24047117 pmcid:PMC3848457 fatcat:tu3xucm2mzhabjqpzl277x64xa

Page 554 of American Society of Civil Engineers. Collected Journals Vol. 121, Issue 7 [page]

1995 American Society of Civil Engineers. Collected Journals  
A simple model for system identification can be developed from discrete time-series analysis, which models the system as an autoregressive—moving-average (ARMA) process, as follows: iS By heoyyeae to eso  ...  Model parameters can be sequentially estimate¢  ... 


2004 Hydroinformatics  
Periodic models are ideal for modeling hydrological time series.  ...  Therefore, an approach to periodic modeling is presented which groups the neighboring days based on cluster analysis, then fits an AR model to each group.  ...  Smoothing each column of the data matrix X in equation (1) by taking several first Fourier harmonics of each column, then the variables of each case for clustering analysis will change smoothly by row,  ... 
doi:10.1142/9789812702838_0165 fatcat:ixd2lifajre5booinqgfwdrmia

Page 2207 of Mathematical Reviews Vol. , Issue 92d [page]

1992 Mathematical Reviews  
Bal (Cluj-Napoca) 65T Numerical methods in Fourier analysis See also 35254, 35255, 65075. 92d:65241 65T20 Bouaoudia, S. (1-CA-E); Marcus, P.S. (1-CA-E) Fast and accurate spectral treatment of coordinate  ...  Nu- merical results show that the method works better with a change of variable bringing the end-points of the integration interval rela- tively closer.  ... 

Page 9196 of Mathematical Reviews Vol. , Issue 2001M [page]

2001 Mathematical Reviews  
We present a sequential observation procedure and find its characteristics associated with false thresholds and delay in the detection of a change point.  ...  (RS-TOMS; Tomsk) Detection of a change point in an autoregression process observed with noise. (Russian. Russian summary) Avtomat. i Telemekh. 2000, no. 3, 76—89; translation in Autom.  ... 

Gearbox Fault Detection Using Synchro-squeezing Transform

Budhaditya Hazra, Sriram Narasimhan
2016 Procedia Engineering  
For robust detection of faults in gear-motors, a fault detection technique based on time-varying autoregressive coefficients of IMFs as features is utilized.  ...  This paper presents a novel fault-detection method for gearbox vibration signatures using synchro-squeezing transform (SST).  ...  a direct application of time and frequency domain methods to the gearbox signals, followed by the use of condition indicators [10] Spectral analysis based on Fourier transform, cepstrum, and envelope  ... 
doi:10.1016/j.proeng.2016.05.023 fatcat:jdepwfbvondkvjzipghskpkzie

Time Series Segmentation Procedures to Detect, Locate and Estimate Change-Points [chapter]

Ana Laura Badagián, Regina Kaiser, Daniel Peña
2014 Empirical Economic and Financial Research  
We are interested in finding changes in the mean and the autoregressive coefficients in piecewise autoregressive processes, as well as changes in the variance of the innovations.  ...  This article deals with the problem of detecting, locating, and estimating the change-points in a time series process.  ...  : : D Â k m ¤ Â k m C1 D : : : D Â T : Most of the parametric methods proposed in the literature for change-point problems consider a normal model.  ... 
doi:10.1007/978-3-319-03122-4_3 fatcat:dqdppblbg5hlfdxx3fx66fsh54

A Kernel-Based Method for Modeling Non-harmonic Periodic Phenomena in Bayesian Dynamic Linear Models

Luong Ha Nguyen, Ianis Gaudot, Shervin Khazaeli, James-A. Goulet
2019 Frontiers in Built Environment  
The results show that the proposed method succeeds in modeling the stationary and non-stationary periodic patterns for both case studies.  ...  To overcome these limitations, this paper proposes a novel approach that combines the existing Bayesian Dynamic Linear Models with a kernel-based method for handling periodic patterns in time series.  ...  Fourier Form The Fourier Form component allows to model sine-like phenomena.  ... 
doi:10.3389/fbuil.2019.00008 fatcat:vxnlz5owjvbnbjzl4bxizrhcnm
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