Noise reduction in chaotic time series data

Pramana ◽  
1999 ◽  
Vol 52 (1) ◽  
pp. 25-37 ◽  
Author(s):  
A. Bhowal ◽  
T. K. Roy
Keyword(s):  
Time Series ◽  
Noise Reduction ◽  
Time Series Data ◽  
Chaotic Time Series ◽  
Series Data

Noise reduction in chaotic time-series data: A survey of common methods

1993 ◽  
Vol 48 (3) ◽  
pp. 1752-1763 ◽  
Author(s):  
Eric J. Kostelich ◽  
Thomas Schreiber
Keyword(s):  
Time Series ◽  
Noise Reduction ◽  
Time Series Data ◽  
Chaotic Time Series ◽  
Series Data

Detection and diagnosis of dynamics in time series data: Theory of noise reduction

1994 ◽  
Author(s):  
Robert Cawley ◽  
Guan-Hson Hsu ◽  
Liming W. Salvino
Keyword(s):  
Time Series ◽  
Noise Reduction ◽  
Time Series Data ◽  
Series Data ◽  
Detection And Diagnosis

Forecast chaotic time series data by DBNs

2014 ◽  
Author(s):  
Takashi Kuremoto ◽  
Masanao Obayashi ◽  
Kunikazu Kobayashi ◽  
Takaomi Hirata ◽  
Shingo Mabu
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Chaotic Time Series ◽  
Series Data

scholarly journals FPGA-Based Implementation of a Multilayer Perceptron Suitable for Chaotic Time Series Prediction

Technologies ◽  
2018 ◽  
Vol 6 (4) ◽  
pp. 90 ◽  
Author(s):  
Ana Pano-Azucena ◽  
Esteban Tlelo-Cuautle ◽  
Sheldon Tan ◽  
Brisbane Ovilla-Martinez ◽  
Luis de la Fraga
Keyword(s):  
Time Series ◽  
Multilayer Perceptron ◽  
Time Series Data ◽  
Mean Squared Error ◽  
Time Series Prediction ◽  
Chaotic Time Series ◽  
Series Data ◽  
Support Vector ◽  
Maximum Lyapunov Exponent ◽  
Inference System

Many biological systems and natural phenomena exhibit chaotic behaviors that are saved in time series data. This article uses time series that are generated by chaotic oscillators with different values of the maximum Lyapunov exponent (MLE) to predict their future behavior. Three prediction techniques are compared, namely: artificial neural networks (ANNs), the adaptive neuro-fuzzy inference system (ANFIS) and least-squares support vector machines (SVM). The experimental results show that ANNs provide the lowest root mean squared error. That way, we introduce a multilayer perceptron that is implemented using a field-programmable gate array (FPGA) to predict experimental chaotic time series.

Topological analysis of chaotic time series data from the Belousov-Zhabotinskii reaction

1991 ◽  
Vol 1 (2) ◽  
pp. 147-173 ◽  
Author(s):  
G. B. Mindlin ◽  
H. G. Solari ◽  
M. A. Natiello ◽  
R. Gilmore ◽  
X. -J. Hou
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Topological Analysis ◽  
Chaotic Time Series ◽  
Series Data ◽  
Belousov Zhabotinskii Reaction

scholarly journals Data-driven approach for noise reduction in pressure-sensitive paint data based on modal expansion and time-series data at optimally placed points

2021 ◽  
Vol 33 (7) ◽  
pp. 077105
Author(s):  
Tomoki Inoue ◽  
Yu Matsuda ◽  
Tsubasa Ikami ◽  
Taku Nonomura ◽  
Yasuhiro Egami ◽  
...  
Keyword(s):  
Time Series ◽  
Noise Reduction ◽  
Time Series Data ◽  
Data Driven ◽  
Series Data ◽  
Pressure Sensitive Paint ◽  
Modal Expansion ◽  
Pressure Sensitive ◽  
Data Driven Approach

Multiple-channel Singular Spectrum Analysis-based noise reduction for MT data observed in Boso Peninsula, Japan

2021 ◽  
Author(s):  
Shu Kaneko ◽  
Katsumi Hattori ◽  
Toru Mogi ◽  
Chie Yoshino
Keyword(s):  
Time Series ◽  
Noise Reduction ◽  
Spectrum Analysis ◽  
Reduction Method ◽  
Time Series Data ◽  
Singular Spectrum Analysis ◽  
Series Data ◽  
Boso Peninsula ◽  
Singular Spectrum ◽  
Kanto Earthquake

<p>Off the coast of the Boso Peninsula, there is a triple junction of the Pacific Plate, the Philippine Sea Plate, and the North American Plate and the Boso Peninsula is one of the seismically active areas in Japan. There are also epicenter areas such as the 1703 Genroku Kanto Earthquake (M8.2), the 1923 Taisho Kanto Earthquake (M7.9), and the Boso Slow Slip which occurs every 6 years, which are geologically interesting places. To estimate the subsurface resistivity structure of the whole Boso area, Magnetotelluric (MT) survey with 41 sites (inter-sites distance of 7 km) has been conducted in 2014-2016, using U43 (12 sites, 1 Hz sampling ; Tierra Technica) and MTU-5, 5A, net (41 sites, 15, 150, and 2400 Hz sampling; Phoenix Geophysics). However, the Boso area is greatly affected by leak current from DC-driven trains, factories, and power lines, so the observed data are contaminated by artificial noises. When we tried to apply the conventional noise reduction method (e.g., remote reference (Gamble et al., 1979) and BIRRP (Chave and Thomson, 2004)) in frequency domain, the obtained MT sounding curve was not ideal. In particular, the phase between the periods of 20 and 400 sec was close to 0 degrees. It suggests that the method used is insufficient to reduce the near-field effect for the Boso data. Thus, we developed a new noise reduction method using MSSA (Multi-channel Singular Spectrum Analysis) as a pre-processing method in time domain.</p><p>The procedure is as follows;</p><p>(1) Decompose 6 component data (Hx, Hy, Ex, Ey, Hxr and Hyr: H and E means magnetic and electric field, respectively, x and y indicates NS and EW component, and r denotes the reference field observed at a quiet station) using MSSA into 6×M principal components (PCs).  Here, M shows the window length of MSSA.</p><p>(2) Check contribution and periods of each PC and eliminate the PCs which are corresponding to the longer periods of variation. That is “detrend” of the original data.</p><p>(3) Apply the second MSSA to the detrended time series data to separate signals and noises shorter than 400 sec.</p><p>(4) Calculating correlation coefficients between H and Hr and between E and Hr for each PC and select the PCs with higher correlation to reconstruct time series data to make MT analysis.</p><p>Then, we perform MT analysis by BIRRP to estimate apparent resistivity,</p><p>As a result, the coherences of H-Hr, and E-Hr were improved and the MT sounding curve became smoother than those results by the conventional noise reduction methods. This indicated that the effectiveness of the proposed noise reduction. However, further investigation in different periods and sites will be required.</p>

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