scholarly journals PARAMETER ESTIMATION FROM TIME-SERIES DATA WITH CORRELATED ERRORS: A WAVELET-BASED METHOD AND ITS APPLICATION TO TRANSIT LIGHT CURVES

2009 ◽  
Vol 704 (1) ◽  
pp. 51-67 ◽  
Author(s):  
Joshua A. Carter ◽  
Joshua N. Winn
Keyword(s):  
Time Series ◽  
Parameter Estimation ◽  
Time Series Data ◽  
Light Curves ◽  
Series Data ◽  
Correlated Errors

scholarly journals De-Trending Time Series Data for Variability Surveys

2008 ◽  
Vol 4 (S253) ◽  
pp. 370-373
Author(s):  
Dae-Won Kim ◽  
Pavlos Protopapas ◽  
Rahul Dave
Keyword(s):  
Time Series ◽  
Clustering Algorithm ◽  
Time Series Data ◽  
Pearson Correlation ◽  
Random Noise ◽  
Light Curves ◽  
Series Data ◽  
Linear Regression Method ◽  
Wide Field ◽  
Noise Sources

AbstractWe present an algorithm for the removal of trends in time series data. The trends could be caused by various systematic and random noise sources such as cloud passages, change of airmass or CCD noise. In order to determine the trends, we select template stars based on a hierarchical clustering algorithm. The hierarchy tree is constructed using the similarity matrix of light curves of stars whose elements are the Pearson correlation values. A new bottom-up merging algorithm is developed to extract clusters of template stars that are highly correlated among themselves, and may thus be used to identify the trends. We then use the multiple linear regression method to de-trend all individual light curves based on these determined trends. Experimental results with simulated light curves which contain artificial trends and events are presented. We also applied our algorithm to TAOS (Taiwan-American Occultation Survey) wide field data observed with a 0.5m f/1.9 telescope equipped with 2k by 2k CCD. With our approach, we successfully removed trends and increased signal to noise in TAOS light curves.

Weibull Parameter Estimation — A Comparison of Different Methods

2005 ◽  
Vol 29 (3) ◽  
pp. 309-315 ◽  
Author(s):  
H Basumatary ◽  
E Sreevalsan ◽  
K K Sasi
Keyword(s):  
Time Series ◽  
Parameter Estimation ◽  
Standard Deviation ◽  
Wind Speed ◽  
Probability Function ◽  
Time Series Data ◽  
Series Data ◽  
Weibull Parameter ◽  
Wind Regime ◽  
Wind Regimes

The Weibull probability function is a widely accepted tool to model wind regimes. This paper presents a comparative study of different methods used to estimate Weibull parameters of a wind regime. Five different methods are described and used for the estimation. Time series data of wind speed over a whole year for two sites have been used for the study. The results obtained as a plot of error versus wind speed are similar in all the five methods, yet the method of standard deviation gives the best results.

ANALYSIS OF WELL WATER LEVEL TIME SERIES DATA FOR AQUIFER PARAMETER ESTIMATION

2018 ◽  
Author(s):  
Martina Rogers ◽  
◽  
Michael C. Sukop ◽  
Michael C. Sukop ◽  
Susan Simmons ◽  
...  
Keyword(s):  
Time Series ◽  
Parameter Estimation ◽  
Water Level ◽  
Time Series Data ◽  
Series Data ◽  
Well Water ◽  
Aquifer Parameter ◽  
Well Water Level

AN AUTOMATED PROCEDURE FOR THE EXTRACTION OF METABOLIC NETWORK INFORMATION FROM TIME SERIES DATA

2006 ◽  
Vol 04 (03) ◽  
pp. 665-691 ◽  
Author(s):  
SIMEONE MARINO ◽  
EBERHARD O. VOIT
Keyword(s):  
Time Series ◽  
Parameter Estimation ◽  
Model Selection ◽  
Model Building ◽  
Time Series Data ◽  
Measurement Techniques ◽  
Extraction Procedure ◽  
System Structure ◽  
Series Data ◽  
Structure Identification

Novel high-throughput measurement techniques in vivo are beginning to produce dense high-quality time series which can be used to investigate the structure and regulation of biochemical networks. We propose an automated information extraction procedure which takes advantage of the unique S-system structure and supports model building from time traces, curve fitting, model selection, and structure identification based on parameter estimation. The procedure comprises of three modules: model Generation, parameter estimation or model Fitting, and model Selection (GFS algorithm).The GFS algorithm has been implemented in MATLAB and returns a list of candidate S-systems which adequately explain the data and guides the search to the most plausible model for the time series under study. By combining two strategies (namely decoupling and limiting connectivity) with methods of data smoothing, the proposed algorithm is scalable up to realistic situations of moderate size. We illustrate the proposed methodology with a didactic example.

scholarly journals Classification of Variable Stars Light Curves Using Long Short Term Memory Network

2021 ◽  
Vol 8 ◽  
Author(s):  
Saksham Bassi ◽  
Kaushal Sharma ◽  
Atharva Gomekar
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Short Term Memory ◽  
Traditional Approach ◽  
Variable Stars ◽  
Light Curves ◽  
Series Data ◽  
Automated Method ◽  
Lstm Network

Owing to the current and upcoming extensive surveys studying the stellar variability, accurate and quicker methods are required for the astronomers to automate the classification of variable stars. The traditional approach of classification requires the calculation of the period of the observed light curve and assigning different variability patterns of phase folded light curves to different classes. However, applying these methods becomes difficult if the light curves are sparse or contain temporal gaps. Also, period finding algorithms start slowing down and become redundant in such scenarios. In this work, we present a new automated method, 1D CNN-LSTM, for classifying variable stars using a hybrid neural network of one-dimensional CNN and LSTM network which employs the raw time-series data from the variable stars. We apply the network to classify the time-series data obtained from the OGLE and the CRTS survey. We report the best average accuracy of 85% and F1 score of 0.71 for classifying five classes from the OGLE survey. We simultaneously apply other existing classification methods to our dataset and compare the results.

Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

2013 ◽  
Author(s):  
Stephen J. Tueller ◽  
Richard A. Van Dorn ◽  
Georgiy Bobashev ◽  
Barry Eggleston
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Time Series Data ◽  
Series Data ◽  
Exploratory Data

Faktor-Faktor Yang Mempengaruhi Nilai Tukar Dollar Amerika Serikat Terhadap Rupiah Tahun 2000–2013

2017 ◽  
Vol 1 (2) ◽  
pp. 177-191
Author(s):  
Rizki Rahma Kusumadewi ◽  
Wahyu Widayat
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Money Supply ◽  
Time Series Data ◽  
The United States ◽  
Economic Conditions ◽  
Series Data ◽  
Arch Model ◽  
United States Dollar ◽  
The Exchange Rate

Exchange rate is one tool to measure a country’s economic conditions. The growth of a stable currency value indicates that the country has a relatively good economic conditions or stable. This study has the purpose to analyze the factors that affect the exchange rate of the Indonesian Rupiah against the United States Dollar in the period of 2000-2013. The data used in this study is a secondary data which are time series data, made up of exports, imports, inflation, the BI rate, Gross Domestic Product (GDP), and the money supply (M1) in the quarter base, from first quarter on 2000 to fourth quarter on 2013. Regression model time series data used the ARCH-GARCH with ARCH model selection indicates that the variables that significantly influence the exchange rate are exports, inflation, the central bank rate and the money supply (M1). Whereas import and GDP did not give any influence.

An Anomaly Detection Method with Exemplar Subsequence for Time Series Data

2016 ◽  
Vol 136 (3) ◽  
pp. 363-372
Author(s):  
Takaaki Nakamura ◽  
Makoto Imamura ◽  
Masashi Tatedoko ◽  
Norio Hirai
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Time Series Data ◽  
Detection Method ◽  
Series Data

The Comparison of Land Subsidence between East and West Side of Bangkok, Thailand

2020 ◽  
Vol 17 (3) ◽  
pp. 1
Author(s):  
Angkana Pumpuang ◽  
Anuphao Aobpaet
Keyword(s):  
Time Series ◽  
Land Subsidence ◽  
Time Series Data ◽  
Series Data ◽  
Industrial Estates ◽  
Vertical Displacements ◽  
Land Deformation ◽  
Extensive Farming ◽  
Chao Phraya River ◽  
Set Up

The land deformation in line of sight (LOS) direction can be measured using time series InSAR. InSAR can successfully measure land subsidence based on LOS in many big cities, including the eastern and western regions of Bangkok which is separated by Chao Phraya River. There are differences in prosperity between both sides due to human activities, land use, and land cover. This study focuses on the land subsidence difference between the western and eastern regions of Bangkok and the most possible cause affecting the land subsidence rates. The Radarsat-2 single look complex (SLC) was used to set up the time series data for long term monitoring. To generate interferograms, StaMPS for Time Series InSAR processing was applied by using the PSI algorithm in DORIS software. It was found that the subsidence was more to the eastern regions of Bangkok where the vertical displacements were +0.461 millimetres and -0.919 millimetres on the western and the eastern side respectively. The districts of Nong Chok, Lat Krabang, and Khlong Samwa have the most extensive farming area in eastern Bangkok. Besides, there were also three major industrial estates located in eastern Bangkok like Lat Krabang, Anya Thani and Bang Chan Industrial Estate. By the assumption of water demand, there were forty-eight wells and three wells found in the eastern and western part respectively. The number of groundwater wells shows that eastern Bangkok has the demand for water over the west, and the pumping of groundwater is a significant factor that causes land subsidence in the area.Keywords: Subsidence, InSAR, Radarsat-2, Bangkok

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