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Spectral estimation for locally stationary time series with missing observations
2011
Statistics and computing
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This article addresses the topic of spectral estimation of a non-stationary time series sampled with missing data. ...
The time series is modelled as a locally stationary wavelet process in the sense introduced by Nason et al (2000) and its realization is assumed to feature missing observations. ...
Young of the Institute of Child Health, Royal Hospital for Sick Children, Bristol for supplying the ECG data. ...
doi:10.1007/s11222-011-9256-x
fatcat:bkxfbehuwvgujdizy3h7ukusf4
A wavelet-based approach for imputation in nonstationary multivariate time series
2021
Statistics and computing
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This article introduces a novel method for data imputation in multivariate nonstationary time series, based on the so-called locally stationary wavelet modelling paradigm. ...
Our methodology is shown to perform well across a range of simulation scenarios, with a variety of missingness structures, as well as being competitive in the stationary time series setting. ...
A range of methods has been developed for spectral estimation in stationary time series with missing values or irregularly sampled observations within the time series and signal processing literature. ...
doi:10.1007/s11222-021-09998-2
fatcat:jij3gl7l3fenrjzhnfrfennrpm
Page 2357 of Mathematical Reviews Vol. , Issue 90D
[page]
1990
Mathematical Reviews
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Summary: “The estimation of the covariances of stationary time se- ries with missing observations is considered. ...
These results are useful for constructing and analyzing parameter or spectrum estimation algorithms based on the sample covariances for stationary time series with missing observations.”
90d:62121 62M10 ...
Page 1025 of Mathematical Reviews Vol. , Issue 91B
[page]
1991
Mathematical Reviews
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Summary: “Much attention has recently been given to long-memory models for use in analyzing many empirical time series with long- range dependence. ...
The same methods are applicable to other functional parameters.”
91b:62195 62M15 60F05 60G10
Dahlhaus, Rainer (D-ESSN)
Nonparametric spectral analysis with missing observations. Sankhya Ser. ...
Nonparametric Spectral Analysis of Multivariate Time Series
2019
Annual Review of Statistics and Its Application
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Spectral analysis of multivariate time series has been an active field of methodological and applied statistics for the past 50 years. ...
In this work, we give a nonexhaustive review of the mostly recent nonparametric methods of spectral analysis of multivariate time series, with an emphasis on model-based approaches. ...
series and Dahlhaus & Polonik (2006) on nonparametric quasi maximum likelihood estimation for locally stationary time series]. ...
doi:10.1146/annurev-statistics-031219-041138
fatcat:rkhudga6zvbxtfyqc7ify4hjya
Analysis of Non-Stationary Modulated Time Series with Applications to Oceanographic Surface Flow Measurements
2017
Journal of Time Series Analysis
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series observations with missing data. ...
Exact inference is often not computationally viable for time series analysis, and so we propose an estimation method based on the Whittle-likelihood, a commonly adopted pseudo-likelihood. ...
Missing observations A particularly enticing use of modulated processes is to account for missing observations in stationary time series. Let {X t : t ∈ N} be a stationary process. ...
doi:10.1111/jtsa.12244
fatcat:n7urd7puxncpji3fljy6hk2hvy
Analysis of nonstationary modulated time series with applications to oceanographic flow measurements
[article]
2017
arXiv
pre-print
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series observations with missing data. ...
Exact inference is often not computationally viable for time series analysis, and so we propose an estimation method based on the Whittle-likelihood, a commonly adopted pseudo-likelihood. ...
Missing observations A particularly enticing use of modulated processes is to account for missing observations in stationary time series. Let {X t : t ∈ N} be a stationary process. ...
arXiv:1605.09107v2
fatcat:z75rndj2ibgzzexo33cpxlhyrq
Bayesian Nonparametric Spectral Estimation
[article]
2019
arXiv
pre-print
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Spectral estimation (SE) aims to identify how the energy of a signal (e.g., a time series) is distributed across different frequencies. ...
In this context, we propose a joint probabilistic model for signals, observations and spectra, where SE is addressed as an exact inference problem. ...
Acknowledgments This work was funded by the projects Conicyt-PIA #AFB170001 Center for Mathematical Modeling and Fondecyt-Iniciación #11171165. ...
arXiv:1809.02196v2
fatcat:4d4w3dpzlrhpjnyh6mrtj6sxee
AdaptSPEC-X: Covariate Dependent Spectral Modeling of Multiple Nonstationary Time Series
[article]
2020
arXiv
pre-print
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We extend AdaptSPEC to handle missing values, a common feature of time series which can cause difficulties for nonparametric spectral methods. A second extension is to allow for a time varying mean. ...
The model can estimate time varying means and spectra at observed and unobserved covariate values, allowing for predictive inference. ...
In a general framework for spectral estimation with stationary time series, Guinness (2019) describes computationally efficient methods for missing data imputation that could be used to simulate from Equation ...
arXiv:1908.06622v2
fatcat:hrkv6kdhcfdt5lxtstltmnfvcu
Spatial long memory
2019
Japanese Journal of Statistics and Data Science
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While a number of contributons have been made, the literature is relatively small and scattered, compared to the literatures on long memory time series on the one hand, and spatial data with short memory ...
We discuss developments and future prospects for statistical modeling and inference for spatial data that have long memory. ...
Compliance with ethical standards Conflict of interest The author states that there is no conflict of interest. ...
doi:10.1007/s42081-019-00061-z
fatcat:zp26ozcmwja4de43zxazr425im
Spectral Analysis of Irregularly Sampled Data with Time Series Models
2009
Open Signal Processing Journal
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A dedicated estimator for time series models of multiple slotted data sets with missing observations has been developed for the estimation of the power spectral density and of the autocorrelation function ...
The algorithm estimates time series models and selects the order and type from a number of candidates. It is tested with benchmark data. ...
Spectral estimation is much simpler for equidistant signals with data missing for than irregular data. ...
doi:10.2174/1876825300801010007
fatcat:fmowg7hdlnbeplh36wv7isutxe
Time series models for spectral analysis of irregular data far beyond the mean data rate
2007
Measurement science and technology
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data sets with missing observations. ...
The algorithm estimates several time series models and selects the best model order and model type from a number of candidates. It is tested with benchmark data. ...
Spectral estimation for equidistant observations with missing data is much simpler than the spectral analysis of continuous irregular data. ...
doi:10.1088/0957-0233/19/1/015103
fatcat:mdwjjhklj5gn5pigioqatpqou4
Page 5324 of Mathematical Reviews Vol. , Issue 94i
[page]
1994
Mathematical Reviews
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The main application is to testing for independence in time series with marginal of unknown form; another is to testing for reversibility in time series.”
{For the entire collection see MR 94c:62005.} ...
94i:62138 62
timation for mixed-spectrum time series is analyzed. ...
A Consistent Method for Learning OOMs from Asymptotically Stationary Time Series Data Containing Missing Values
[article]
2018
arXiv
pre-print
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Recently, a spectral OOM learning algorithm for time series with missing data was introduced and proved to be consistent, albeit under quite strong conditions. ...
In the traditional framework of spectral learning of stochastic time series models, model parameters are estimated based on trajectories of fully recorded observations. ...
Herbert Jaeger for his valuable suggestions throughout this project and for his careful proofreading of the final manuscript. The author thanks Dr. Michael Thon for helpful discussions. ...
arXiv:1808.03873v2
fatcat:ookqq6ir5vehvflj3e2tpu5ci4
Theoretical and Practical Limits of Kolmogorov-Zurbenko Periodograms with DiRienzo-Zurbenko Algorithm Smoothing in the Spectral Analysis of Time Series Data
[article]
2020
arXiv
pre-print
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The Kolomogorov-Zurbenko periodogram with DiRienzo-Zurbenko algorithm smoothing is the state-of-the-art method for spectral analysis of time series data. ...
approach, with support also garnered for its being robust even in the face of significant levels of missing data. ...
For an ordinary stationary process , the spectral density is a function of a Fourier transform of the time series dataset, a selected spectral window form, and a selected spectral window width. ...
arXiv:2007.03031v1
fatcat:hyg3pb75cvd2to2kixnbc3ypca
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