Determination of electrochemical impedance of lithium ion battery from time series data by wavelet transformation -Uncertainty of resolutions in time and frequency domains-

2020 ◽  
Vol 332 ◽  
pp. 135462
Author(s):  
Masayuki Itagaki ◽  
Yusuke Gamano ◽  
Yoshinao Hoshi ◽  
Isao Shitanda
Keyword(s):  
Time Series ◽  
Lithium Ion Battery ◽  
Wavelet Transformation ◽  
Time Series Data ◽  
Electrochemical Impedance ◽  
Lithium Ion ◽  
Series Data ◽  
Frequency Domains ◽  
Time And Frequency Domains

scholarly journals CHARACTERIZATION OF LAND COVER SEASONALITY IN SENTINEL-1 TIME SERIES DATA

2020 ◽  
Vol V-3-2020 ◽  
pp. 97-104
Author(s):  
C. Dubois ◽  
M. M. Mueller ◽  
C. Pathe ◽  
T. Jagdhuber ◽  
F. Cremer ◽  
...  
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Time Series Data ◽  
Coniferous Forest ◽  
Series Data ◽  
Urban Land Cover ◽  
Multi Temporal ◽  
Forested Areas

Abstract. In this study, we analyze Sentinel-1 time series data to characterize the observed seasonality of different land cover classes in eastern Thuringia, Germany and to identify multi-temporal metrics for their classification. We assess the influence of different polarizations and different pass directions on the multi-temporal backscatter profile. The novelty of this approach is the determination of phenological parameters, based on a tool that has been originally developed for optical imagery. Furthermore, several additional multitemporal metrics are determined for the different classes, in order to investigate their separability for potential multi-temporal classification schemes. The results of the study show a seasonality for vegetation classes, which differs depending on the considered class: whereas pastures and broad-leaved forests show a decrease of the backscatter in VH polarization during summer, an increase of the backscatter in VH polarization is observed for coniferous forest. The observed seasonality is discussed together with meteorological information (precipitation and air temperature). Furthermore, a dependence of the backscatter of the pass direction (ascending/descending) is observed particularly for the urban land cover classes. Multi-temporal metrics indicate a good separability of principal land cover classes such as urban, agricultural and forested areas, but further investigation and use of seasonal parameters is needed for a distinct separation of specific forest sub-classes such as coniferous and deciduous.

scholarly journals Peramalan Banyak Pengunjung Pantai Pandasimo Bantul Menggunakan Regresi Runtun Waktu dan Seasonal Autoregressive Integrated Moving Average Exogenous

2021 ◽  
Vol 4 (1) ◽  
pp. 57
Author(s):  
Tito Tatag Prakoso ◽  
Etik Zukhronah ◽  
Hasih Pratiwi
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Seasonal Pattern ◽  
Series Data ◽  
Gregorian Calendar ◽  
Time Series Regression ◽  
Autoregressive Integrated Moving Average ◽  
Past Data

<p>Forecasting is a ways to predict what will happen in the future based on the data in the past. Data on the number of visitors in Pandansimo beach are time series data. The pattern of the number of visitors in Pandansimo beach is influenced by holidays, so it looks like having a seasonal pattern. The majority of Indonesian citizens are Muslim who celebrate Eid Al-Fitr in every year. The determination of Eid Al-Fitr does not follow the Gregorian calendar, but based on the Lunar calendar. The variation of the calendar is about the determination of Eid Al-Fitr which usually changed in the Gregorian calendar, because in the Gregorian calendar, Eid Al-Fitr day will advance one month in every three years. Data that contain seasonal and calendar variations can be analyzed using time series regression and Seasonal Autoregressive Integrated Moving Average Exogenous  (SARIMAX) models. The aims of this study are to obtain a better model between time series regression and SARIMAX and to forecast the number of Pandansimo beach visitors using a better model. The result of this study indicates that the time series regression model is a better model. The forecasting from January to December 2018 in succession are 13255, 6674, 8643, 7639, 13255, 8713, 22635, 13255, 13255, 9590, 8549, 13255 visitors.</p><strong>Keywords: </strong>time series regression, seasonal, calendar variations, SARIMAX, forecasting

MULTIPLE‐CONSTRAINT ADAPTIVE FILTERING

Geophysics ◽  
1971 ◽  
Vol 36 (3) ◽  
pp. 498-509 ◽  
Author(s):  
Aaron Booker ◽  
Chung‐Yen Ong
Keyword(s):  
Time Series ◽  
Data Processing ◽  
Adaptive Filtering ◽  
Time Series Data ◽  
Seismic Array ◽  
Wiener Filtering ◽  
Series Data ◽  
Noise Sources ◽  
Multiple Constraint

An algorithm is derived for multichannel time‐series data processing, which maintains specified initial multiple filter constraints for known signal or noise sources while simultaneously adapting the filter to minimize the effect of the unknown noise field. Problems of implementing the technique such as convergence, determination of a starting filter, and comparison of results with conventional filters are discussed and illustrated with data from a vertical seismic array. The procedure is shown to be stable and obtains approximately 3–4 db gain in S/N improvement over conventional Wiener filtering in the band 1 to 3 hz.

A SIMPLE TEST FOR HIDDEN PERIODICITY IN TIME SERIES DATA

1995 ◽  
Vol 05 (01) ◽  
pp. 265-269 ◽  
Author(s):  
MICHAEL ROSENBLUM ◽  
JÜRGEN KURTHS
Keyword(s):  
Experimental Data ◽  
Time Series ◽  
Spectral Analysis ◽  
Additive Noise ◽  
Time Series Data ◽  
Series Data ◽  
Simple Test ◽  
Data Set ◽  
Dynamical Characteristics

We would like to draw the attention of specialists in time series analysis to a simple but efficient algorithm for the determination of hidden periodic regimes in complex time series. The algorithm is stable towards additive noise and allows one to detect periodicity even if the examined data set contains only a few periods. In such cases it is more suitable than other techniques, such as spectral analysis or recurrence map. We recommend the use of this test prior to the evaluation of attractor dimensions and other dynamical characteristics from experimental data.

Determination of input nodes for neural network model in time series data

2020 ◽  
Vol 22 (3) ◽  
pp. 1055-1065
Author(s):  
Hyeonjung Park ◽  
Seungbae Choi ◽  
Changwan Kang
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Network Model ◽  
Neural Network Model ◽  
Time Series Data ◽  
Series Data

scholarly journals Monte Carlo Optimization for Sliding Window Size in Dixon Quality Control of Environmental Monitoring Time Series Data

2020 ◽  
Vol 10 (5) ◽  
pp. 1876
Author(s):  
Zhongya Fan ◽  
Huiyun Feng ◽  
Jingang Jiang ◽  
Changjin Zhao ◽  
Ni Jiang ◽  
...  
Keyword(s):  
Water Quality ◽  
Time Series ◽  
Monte Carlo ◽  
Environmental Monitoring ◽  
Time Series Data ◽  
Window Size ◽  
Sliding Window ◽  
Series Data ◽  
Monitoring Time

Outliers are often present in large datasets of water quality monitoring time series data. A method of combining the sliding window technique with Dixon detection criterion for the automatic detection of outliers in time series data is limited by the empirical determination of sliding window sizes. The scientific determination of the optimal sliding window size is very meaningful research work. This paper presents a new Monte Carlo Search Method (MCSM) based on random sampling to optimize the size of the sliding window, which fully takes advantage of computers and statistics. The MCSM was applied in a case study to automatic monitoring data of water quality factors in order to test its validity and usefulness. The results of comparing the accuracy and efficiency of the MCSM show that the new method in this paper is scientific and effective. The experimental results show that, at different sample sizes, the average accuracy is between 58.70% and 75.75%, and the average computation time increase is between 17.09% and 45.53%. In the era of big data in environmental monitoring, the proposed new methods can meet the required accuracy of outlier detection and improve the efficiency of calculation.

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