scholarly journals Mining Time Series Data with Two Dimensional Fuzzy Pattern Rules

2015 ◽  
Author(s):  
Haifeng Xia ◽  
Bing Chen ◽  
Jiawei Fan ◽  
Zhi Li ◽  
Dan Gao
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Two Dimensional ◽  
Fuzzy Pattern

scholarly journals Multi-scale analysis of linear data in a two-dimensional space

2013 ◽  
Vol 13 (3) ◽  
pp. 248-265 ◽  
Author(s):  
Yi Qiang ◽  
Seyed H Chavoshi ◽  
Steven Logghe ◽  
Philippe De Maeyer ◽  
Nico Van de Weghe
Keyword(s):  
Time Series ◽  
Visual Analysis ◽  
Time Series Data ◽  
Dimensional Space ◽  
Series Data ◽  
Multiple Time ◽  
Two Dimensional ◽  
Map Algebra ◽  
Real World Datasets ◽  
Triangular Model

Many disciplines are faced with the problem of handling time-series data. This study introduces an innovative visual representation for time series, namely the continuous triangular model. In the continuous triangular model, all subintervals of a time series can be represented in a two-dimensional continuous field, where every point represents a subinterval of the time series, and the value at the point is derived through a certain function (e.g. average or summation) of the time series within the subinterval. The continuous triangular model thus provides an explicit overview of time series at all different scales. In addition to time series, the continuous triangular model can be applied to a broader sense of linear data, such as traffic along a road. This study shows how the continuous triangular model can facilitate the visual analysis of different types of linear data. We also show how the coordinate interval space in the continuous triangular model can support the analysis of multiple time series through spatial analysis methods, including map algebra and cartographic modelling. Real-world datasets and scenarios are employed to demonstrate the usefulness of this approach.

Band Centers and Track Finding for Large Two-Dimensional Infrared Spectroscopic Arrays

2003 ◽  
Vol 57 (3) ◽  
pp. 323-330 ◽  
Author(s):  
Li Chen ◽  
Marc Garland
Keyword(s):  
Time Series ◽  
Spectral Data ◽  
Time Series Data ◽  
Series Data ◽  
Second Derivative ◽  
Two Dimensional ◽  
Peak Finding ◽  
Derivative Method ◽  
Computationally Intensive ◽  
Second Derivative Method

An efficient two-dimensional (2D) peak-finding algorithm is proposed to find peak maps that specify the peak centers of all bands in two-dimensional arrays of time-series infrared spectral data. The algorithm combines the second-derivative method with the intrinsic characteristics of 2D infrared reaction spectral data. Initially, the second-derivative method is used to detect all possible peak center positions, and then three criteria drawn from characteristics of 2D continuous spectral data are employed to filter peak positions. Four 2D peak maps are generated in a sequential order, with better and better approximations to the peak center positions being obtained in each. The 2D peak-finding algorithm has been successfully applied to both simulated spectra (to initially evaluate the algorithm) and then real 2D experimental spectra. The resulting peak maps exhibit very good estimates of the peak center positions. An ordering from the most significant to the least significant bands is obtained. The final peak maps can be used as starting parameters for various applications including the computationally intensive curve-fitting of time-series data.

Wave-SOM

2010 ◽  
Vol 1 (2) ◽  
pp. 50-73 ◽  
Author(s):  
Andrew Blanchard ◽  
Christopher Wolter ◽  
David S. McNabb ◽  
Eitan Gross
Keyword(s):  
Oxidative Stress ◽  
Time Series ◽  
Ribosomal Proteins ◽  
Clustering Algorithm ◽  
Time Series Data ◽  
Expression Patterns ◽  
Series Data ◽  
Self Organizing Map ◽  
Two Dimensional ◽  
Yeast Genes

In this paper, the authors present a wavelet-based algorithm (Wave-SOM) to help visualize and cluster oscillatory time-series data in two-dimensional gene expression micro-arrays. Using various wavelet transformations, raw data are first de-noised by decomposing the time-series into low and high frequency wavelet coefficients. Following thresholding, the coefficients are fed as an input vector into a two-dimensional Self-Organizing-Map clustering algorithm. Transformed data are then clustered by minimizing the Euclidean (L2) distance between their corresponding fluctuation patterns. A multi-resolution analysis by Wave-SOM of expression data from the yeast Saccharomyces cerevisiae, exposed to oxidative stress and glucose-limited growth, identified 29 genes with correlated expression patterns that were mapped into 5 different nodes. The ordered clustering of yeast genes by Wave-SOM illustrates that the same set of genes (encoding ribosomal proteins) can be regulated by two different environmental stresses, oxidative stress and starvation. The algorithm provides heuristic information regarding the similarity of different genes. Using previously studied expression patterns of yeast cell-cycle and functional genes as test data sets, the authors’ algorithm outperformed five other competing programs.

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
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