Time series analysis of Holocene climate data

1990 ◽  
Vol 330 (1615) ◽  
pp. 601-616 ◽  
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Time Domain ◽  
Small Sample ◽  
Climate Data ◽  
Holocene Climate ◽  
Series Analysis ◽  
Window Method ◽  
Band Limited ◽  
Periodic Components

Holocene climate records are imperfect proxies for processes containing complicated mixtures of periodic and random signals. I summarize time series analysis methods for such data with emphasis on the multiple-data-window technique. This method differs from conventional approaches to time series analysis in that a set of data tapers is applied to the data in the time domain before Fourier transforming. The tapers, or data windows, are discrete prolate spheroidal sequences characterized as being the most nearly band-limited functions possible among functions defined on a finite time domain. The multiple-window method is a small-sample theory and essentially an inverse method applied to the finite Fourier transform. For climate data it has the major advantage of providing a narrowband F -test for the presence and significance of periodic components and of being able to separate them from the non-deterministic part of the process. Confidence intervals for the estimated quantities are found by jackknifing across windows. Applied to 14 C records, this method confirms the presence of the ‘Suess wiggles’ and give an estimated period of 208.2 years. Analysis of the thickness variations of bristlecone pine growth rings shows a general absence of direct periodic components but a variation in the structure of the time series with a 2360-year period.

scholarly journals Multi-Scale Entropy Analysis as a Method for Time-Series Analysis of Climate Data

Climate ◽  
2015 ◽  
Vol 3 (1) ◽  
pp. 227-240 ◽  
Author(s):  
Heiko Balzter ◽  
Nicholas Tate ◽  
Jörg Kaduk ◽  
David Harper ◽  
Susan Page ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Entropy Analysis ◽  
Climate Data ◽  
Multi Scale ◽  
Series Analysis

scholarly journals Interactive Education for Time-Domain Time Series Analysis using ITTSAE

2015 ◽  
Vol 48 (28) ◽  
pp. 751-756
Author(s):  
J.M. DÍaz ◽  
S. Dormido ◽  
D.E. Rivera
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Time Domain ◽  
Series Analysis ◽  
Interactive Education

A New Method of Time Series Analysis and its Application to Identifying Modal Parameters

1991 ◽  
Author(s):  
Yusheng He ◽  
Zhaoxiang Deng
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Time Domain ◽  
New Method ◽  
Modal Parameters ◽  
Frequency Domain Method ◽  
Identification Algorithm ◽  
Series Analysis ◽  
The Time Domain ◽  
Modeling Strategy

Abstract In the paper, the attention concentrates on the time domain modal analysis. A new method of time series analysis, which is formed mainly by an ideal modeling strategy and a new COR-IV method, is developed. In addition, an interesting parameter called as modal energy ratio, which is available for design reference, is defined and its identification algorithm is given. The new method presented in this paper and Frequency Domain Method (FDM) are performed on a frame of SG120 vehicle. It is shown by comparison between these two methods that the new method of time series analysis is practical.

The Time Domain and the Frequency Domain in Time Series Analysis

1968 ◽  
Vol 70 (1) ◽  
pp. 25 ◽  
Author(s):  
O. Brandes ◽  
J. Farley ◽  
M. Hinich ◽  
U. Zackrisson
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Frequency Domain ◽  
Time Domain ◽  
Series Analysis ◽  
The Time Domain

scholarly journals A time series analysis of permanent GNSS stations in the northwest network of Iran

2021 ◽  
Vol 64 (2) ◽  
Author(s):  
Seyed Amin Ghasemi Khalkhali ◽  
Alireza A. Ardalan ◽  
Roohollah Karimi
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Deterministic Model ◽  
Noise Analysis ◽  
Model Analysis ◽  
Deformation Model ◽  
Series Analysis ◽  
Nonlinear Trend ◽  
Seismic Deformation ◽  
Periodic Components

The aim of this study is to estimate reliable velocities along with their realistic uncertainties based on a robust time series analysis including analysis of deterministic and stochastic (noise) models. In the deterministic model analysis part, we use a complete station motion model comprised of jump effects, linear and nonlinear trend, periodic components, and post-seismic deformation model. This part also consists of jump detection, outlier detection, and statistical significance of jumps. We perform the deterministic model analysis in an iterative process to elevate its efficiency. In the noise analysis part, first, we remove the spatial correlation of observations using the weighted stacking method based on the common mode error (CME) parameter. Next, a combination of white and flicker noises is used to determine the stochastic model. This time series analysis is applied for 11-year time series of 25 permanent GNSS stations from 2006 to 2016 in the northwest network of Iran. We reveal that there is a nonlinear trend in some stations, although most stations have a linear trend. In addition, we found that a combination of logarithmic and exponential functions is the most appropriate post-seismic deformation model in our study region. The result of the noise analysis shows that the spatial filtering reduces the norm of post-fit residual vector by 19.34%, 17.51%, and 12.44% on average for the east, north, and up components, respectively. Furthermore, the uncertainties obtained from the combination of white and flicker noises at the east, north, and up components are 5.0, 4.8, and 4.4 times greater than those of the white noise model, respectively. The results indicate that the stations move horizontally with an average velocity of 36.0 ± 0.3 mm/yr in the azimuth of 52.66° NE which is compatible with velocities obtained from MIDAS. We obtained the vertical velocity of most stations in the range of -5 to 5 mm/yr. However, in three stations of GGSH, ORYH, and BNAB, which are in the proximity of Lake Urmia, the vertical velocities are estimated to be -80.9 mm/yr, -50.6 mm/yr, and -11.4 mm/yr, respectively. Moreover, we found that these three stations possess large periodic signal amplitudes in all three coordinate components as well as a nonlinear trend in the up component.

Damage Identification of Simply Supported Beam Bridge Based on Time Series Analysis

2012 ◽  
Vol 236-237 ◽  
pp. 617-621
Author(s):  
Han Bing Liu ◽  
Yan Jun Song ◽  
Guo Jin Tan ◽  
Yan Yi Sun
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Dynamic Response ◽  
Time Domain ◽  
Damage Identification ◽  
Simply Supported Beam ◽  
Simply Supported ◽  
Series Analysis ◽  
Wide Range ◽  
Beam Bridge

Presently, the study on damage identification of bridges is very popular and it has a wide range of applications. Also the related theory and technology are constantly developing and mature. The researches based on the dynamic response of bridge in frequency domain is more, but the dynamics theory is complex and difficult for the engineering personnel to grasp. On the opposite, although the damage identification method based on the dynamic response of bridge in time domain is easy to understand, it is difficulty for applications. The Auto Regressive Moving Average model (ARMA) of time series analysis can be used to settle this problem. It is a not very abstruse theory and it is already apply for the system identification of some Structures. In this paper, we use time series analysis for the damage identification of simply supported beam bridge combined with its own dynamic response in time domain.

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