scholarly journals Representing Unevenly-Spaced Time Series Data for Visualization and Interactive Exploration

2005 ◽  
pp. 835-846 ◽  
Author(s):  
Aleks Aris ◽  
Ben Shneiderman ◽  
Catherine Plaisant ◽  
Galit Shmueli ◽  
Wolfgang Jank
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Interactive Exploration

scholarly journals Dynamic Query Tools for Time Series Data Sets: Timebox Widgets for Interactive Exploration

2004 ◽  
Vol 3 (1) ◽  
pp. 1-18 ◽  
Author(s):  
Harry Hochheiser ◽  
Ben Shneiderman
Keyword(s):  
Time Series ◽  
User Interfaces ◽  
Time Series Data ◽  
Series Data ◽  
Time Interval ◽  
Data Sets ◽  
Multiple Time ◽  
Interactive Exploration ◽  
Additional Support ◽  
Dynamic Query

Timeboxes are rectangular widgets that can be used in direct-manipulation graphical user interfaces (GUIs) to specify query constraints on time series data sets. Timeboxes are used to specify simultaneously two sets of constraints: given a set of N time series profiles, a timebox covering time periods x1… x2 ( x1 ≤ x2) and values y1… y2 ( y1 ≤ y2) will retrieve only those n√N that have values y1 ≤ y2 during all times x1 ≤ x ≤ x2. TimeSearcher is an information visualization tool that combines timebox queries with overview displays, query-by-example facilities, and support for queries over multiple time-varying attributes. Query manipulation tools including pattern inversion and ‘leaders & laggards’ graphical bookmarks provide additional support for interactive exploration of data sets. Extensions to the basic timebox model that provide additional expressivity include variable time timeboxes, which can be used to express queries with variability in the time interval, and angular queries, which search for ranges of differentials, rather than absolute values. Analysis of the algorithmic requirements for providing dynamic query performance for timebox queries showed that a sequential search outperformed searches based on geometric indices. Design studies helped identify the strengths and weaknesses of the query tools. Extended case studies involving the analysis of two different types of data from molecular biology experiments provided valuable feedback and validated the utility of both the timebox model and the TimeSearcher tool. Timesearcher is available at http://www.cs.umd.edu/hcil/timesearcher

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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