Hybrid Evolutionary Algorithm for Rule Set Discovery in Time-Series Data to Forecast and Explain Algal Population Dynamics in Two Lakes Different in Morphometry and Eutrophication

2006 ◽  
pp. 347-367 ◽  
Author(s):  
H. Cao ◽  
F. Recknagel ◽  
B. Kim ◽  
N. Takamura
Keyword(s):  
Time Series ◽  
Population Dynamics ◽  
Evolutionary Algorithm ◽  
Time Series Data ◽  
Series Data ◽  
Algal Population ◽  
Hybrid Evolutionary Algorithm ◽  
Rule Set

Analysis of Time-Series Data by Merging Decision Rules

2017 ◽  
Vol 21 (6) ◽  
pp. 1026-1033
Author(s):  
Yoshiyuki Matsumoto ◽  
Junzo Watada ◽  
◽  
Keyword(s):  
Time Series ◽  
Decision Rule ◽  
Set Theory ◽  
Rough Set ◽  
Time Series Data ◽  
Rough Set Theory ◽  
Decision Rules ◽  
Series Data ◽  
Rule Set ◽  
Analysis Of Time Series

Rough set theory was proposed by Z. Pawlak in 1982. This theory enables the mining of knowledge granules as decision rules from a database, the web, and other sources. This decision rule set can then be used for data analysis. We can apply the decision rule set to reason, estimate, evaluate, or forecast an unknown object. In this paper, rough set theory is used for the analysis of time-series data. We propose a method to acquire rules from time-series data using regression. The trend of the regression line can be used as a condition attribute. We predict the future slope of the time-series data as decision attributes. We also use merging rules to further analyze the time series data.

scholarly journals Inferring population dynamics from single-cell RNA-sequencing time series data

2019 ◽  
Vol 37 (4) ◽  
pp. 461-468 ◽  
Author(s):  
David S. Fischer ◽  
Anna K. Fiedler ◽  
Eric M. Kernfeld ◽  
Ryan M. J. Genga ◽  
Aimée Bastidas-Ponce ◽  
...  
Keyword(s):  
Time Series ◽  
Population Dynamics ◽  
Single Cell ◽  
Rna Sequencing ◽  
Time Series Data ◽  
Series Data ◽  
Single Cell Rna Sequencing

scholarly journals Using time series data to assess recent population dynamics of Bald Eagles in the northeast United States

Ecosphere ◽  
2019 ◽  
Vol 10 (11) ◽  
Author(s):  
Brenda J. Hanley ◽  
André A. Dhondt ◽  
Brian Dennis ◽  
Krysten L. Schuler
Keyword(s):  
United States ◽  
Time Series ◽  
Population Dynamics ◽  
Time Series Data ◽  
Series Data ◽  
Bald Eagles ◽  
Northeast United States

scholarly journals A Multiscale Spatial Dataset for Policy-Driven Land Developability across the United States, 2001–2011

2020 ◽  
Author(s):  
Hung Chak Ho ◽  
Guangqing Chi
Keyword(s):  
United States ◽  
Time Series ◽  
Population Dynamics ◽  
Health Risks ◽  
Time Series Data ◽  
Land Development ◽  
The United States ◽  
Land Policy ◽  
Series Data ◽  
Spatial Dataset

Abstract. Land vulnerability and development can be restricted by both land policy and geophysical limits. Land vulnerability and development cannot be simply quantified by land cover/use change, because growth related to population dynamics is not horizontal. Particularly, time-series data with a higher flexibility considering the ability of land to be developed should be used to identify areas of spatiotemporal change. By considering the policy aspects of land development, this approach will allow one to further identify the lands facing population stress, socioeconomic burdens, and health risks. Here the concept of “land developability” is expanded to include policy-driven factors and land vulnerability to better reconcile developability with socio-environmental justice. The first phrase of policy-driven land developability mapping is implemented in estimating land information across the contiguous United States in 2001, 2006, and 2011. Multiscale data products for state-, county- and census-tract-levels are provided from this estimation. The extension of this approach can be applied to other countries with modifications for their specific scenarios. The data generated from this work are available at https://doi.org/10.7910/DVN/AMZMWH (Chi and Ho, 2019).

scholarly journals Population Dynamics of Aphids on Cereals: Digging in the Time-Series Data to Reveal Population Regulation Caused by Temperature

PLoS ONE ◽  
2014 ◽  
Vol 9 (9) ◽  
pp. e106228 ◽  
Author(s):  
Marek Brabec ◽  
Alois Honěk ◽  
Stano Pekár ◽  
Zdenka Martinková
Keyword(s):  
Time Series ◽  
Population Dynamics ◽  
Population Regulation ◽  
Time Series Data ◽  
Series Data

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
Sign in / Sign up

Export Citation Format

Share Document