A Novel Method for Topological Embedding of Time-Series Data

Most of the existing change detection methods using full polarimetric synthetic aperture radar (PolSAR) are limited to detecting change between two points in time. In this paper, a novel method was proposed to detect the change based on time-series data from different sensors. Firstly, the overall difference image of a time-series PolSAR was calculated by ominous statistic test. Secondly, difference images between any two images in different times ware acquired by R<sub>j</sub> statistic test. Generalized Gaussian mixture model (GGMM) was used to obtain time-series change detection maps in the last step for the proposed method. To verify the effectiveness of the proposed method, we carried out the experiment of change detection by using the time-series PolSAR images acquired by Radarsat-2 and Gaofen-3 over the city of Wuhan, in China. Results show that the proposed method can detect the time-series change from different sensors.

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Detecting and quantifying causal associations in large nonlinear time series datasets

Science Advances ◽

10.1126/sciadv.aau4996 ◽

2019 ◽

Vol 5 (11) ◽

pp. eaau4996 ◽

Cited By ~ 31

Author(s):

Jakob Runge ◽

Peer Nowack ◽

Marlene Kretschmer ◽

Seth Flaxman ◽

Dino Sejdinovic

Keyword(s):

Time Series ◽

Large Scale ◽

Time Series Data ◽

Series Data ◽

Real World Data ◽

Physical Mechanisms ◽

Research Fields ◽

Causal Networks ◽

Novel Method ◽

Synthetic Datasets

Identifying causal relationships and quantifying their strength from observational time series data are key problems in disciplines dealing with complex dynamical systems such as the Earth system or the human body. Data-driven causal inference in such systems is challenging since datasets are often high dimensional and nonlinear with limited sample sizes. Here, we introduce a novel method that flexibly combines linear or nonlinear conditional independence tests with a causal discovery algorithm to estimate causal networks from large-scale time series datasets. We validate the method on time series of well-understood physical mechanisms in the climate system and the human heart and using large-scale synthetic datasets mimicking the typical properties of real-world data. The experiments demonstrate that our method outperforms state-of-the-art techniques in detection power, which opens up entirely new possibilities to discover and quantify causal networks from time series across a range of research fields.

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A Novel Method for Analyzing [Ca2+] Flux Kinetics in High-Throughput Screening

CrossRef Listing of Deleted DOIs ◽

10.1177/1087057106287929 ◽

2006 ◽

Vol 11 (5) ◽

pp. 511-518 ◽

Cited By ~ 5

Author(s):

Philip Gribbon ◽

Chris Chambers ◽

Kaupo Palo ◽

Juergen Kupper ◽

Juergen Mueller ◽

...

Keyword(s):

Time Series ◽

High Throughput ◽

High Throughput Screening ◽

Time Series Data ◽

Calcium Flux ◽

Series Data ◽

Imaging Plate ◽

Additional Information ◽

Novel Method ◽

G Protein Coupled

Driven by multiparameter fluorescence readouts and the analysis of kinetic responses from biological assay systems, the amount and complexity of high-throughput screening data are constantly increasing. As a consequence, the reduction of data to a simple number, reflecting a percentage activity/inhibition, is no longer an adequate approach because valuable additional information, for example, about compound-or process-induced artifacts, is lost. Time series data such as the transient calcium flux observed after activation of Gq-coupled G protein-coupled receptors (GPCRs), are especially challenging with respect to quantity of data; typically, responses are followed for several minutes. Based on measurements taken on the fluorometric imaging plate reader, the authors have introduced a mathematical model to describe the time traces of cellular calcium fluxes mediated by the activation of GPCRs. The model describes the time series using 13 parameters, reducing the amount of data by 90% while guiding the detection of compound-induced artifacts as well as the selection of compounds for further characterization.

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A Novel Method for Similarity Search over Meteorological Time Series Data based on the Coulomb’s Law

Proceedings of the 15th International Conference on Enterprise Information Systems ◽

10.5220/0004446702090216 ◽

2013 ◽

Keyword(s):

Time Series ◽

Similarity Search ◽

Time Series Data ◽

Series Data ◽

Coulomb’S Law ◽

Novel Method ◽

Coulomb's Law

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Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

PsycEXTRA Dataset ◽

10.1037/e634372013-001 ◽

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

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

Jurnal Riset Manajemen Sekolah Tinggi Ilmu Ekonomi Widya Wiwaha Program Magister Manajemen ◽

10.32477/jrm.v1i2.72 ◽

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.

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