Noise Reduction for Time-Domain Sensing Signal of Brillouin Scattering Based on Time Series Analysis and Kalman Filter Algorithm

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.

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Time Series Analysis with a Skewed Kalman Filter

Skew-Elliptical Distributions and Their Applications ◽

10.1201/9780203492000-15 ◽

2004 ◽

pp. 269-288

Keyword(s):

Time Series ◽

Kalman Filter ◽

Time Series Analysis ◽

Series Analysis

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Kalman Filter and Financial Time Series Analysis

Eco-friendly Computing and Communication Systems - Communications in Computer and Information Science ◽

10.1007/978-3-642-32112-2_40 ◽

2012 ◽

pp. 339-351 ◽

Cited By ~ 3

Author(s):

M. P. Rajan ◽

Jimson Mathew

Keyword(s):

Time Series ◽

Kalman Filter ◽

Time Series Analysis ◽

Financial Time Series ◽

Financial Time ◽

Series Analysis

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Interactive Education for Time-Domain Time Series Analysis using ITTSAE

IFAC-PapersOnLine ◽

10.1016/j.ifacol.2015.12.220 ◽

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

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

13th Biennial Conference on Mechanical Vibration and Noise: Vibration Analysis — Analytical and Computational ◽

10.1115/detc1991-0317 ◽

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.

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