Association Networks in Time Series Data Mining

“…The objective of the paper is to apply novel time series data mining techniques based on local trend associations [12] to pollutant time series in order to obtain information about possible associations between meteorological conditions and air pollution for different year seasons in three meteorological stations located in MCMA. Meteorological conditions near these stations differ one from another depending on wind strength and wind direction.…”

Section: Introductionmentioning

confidence: 99%

On Trend Association Analysis of Time Series of Atmospheric Pollutants and Meteorological Variables in Mexico City Metropolitan Area

Almanza

Batyrshin

2011

Lecture Notes in Computer Science

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The paper studies trend associations between atmospheric pollutants and meteorological variables time series of Mexico City Metropolitan Area (MCMA) by applying the Moving Approximation Transform (MAP). This recently introduced technique measures and visualizes associations of the dynamics between different time series in the form of an association network. The paper studies associations between 5 atmospheric pollutants (SO 2 , O 3, NO 2 , NOx and PM 2.5) and 7 meteorological variables (mean wind velocity, minimum, average and maximum values of both temperature and relative humidity) measured daily during one year in three meteorological stations located in different zones of MCMA. These associations were studied for 4 seasons characterized by different meteorological conditions. For considered stations atmospheric pollutants and meteorological variables for different seasons positive and negative associations have been found and explained.

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“…30 Boyd 31 and Batyrshin and Wagenknecht 25 propose a system-generating linguistic descriptions of time series as IF-THEN rules, with fuzzy intervals as values in the case of the latter work. A rule-based description of times series via linguistic (fuzzy) summaries by using the so-called moving approximation transform is given by Batyrshin et al 26,27 Baldwin et al 32 propose a model time series by using linguistic shape descriptors represented by parametrized functions and use the FRIL system based on evidential reasoning for prediction. Höppner 33 discusses the derivation of temporal relations between shape patterns using a segmentation of time series and a transformation into sequences of state intervals (increasing, decreasing, constant, and so on) and presents some applications.…”

Section: Introductionmentioning

confidence: 99%

An approach to the linguistic summarization of time series using a fuzzy quantifier driven aggregation

Kacprzyk

Wilbik

Zadrożny

2010

Int. J. Intell. Syst.

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We extend our previous work on the linguistic summarization of time series data meant as the linguistic summarization of trends, i.e. consecutive parts of the time series, which may be viewed as exhibiting a uniform behavior under an assumed (degree of) granulation, and identified with straight line segments of a piecewise linear approximation of the time series. We characterize the trends by the dynamics of change, duration, and variability. A linguistic summary of a time series is then viewed to be related to a linguistic quantifier driven aggregation of trends. We primarily employ for this purpose the classic Zadeh's calculus of linguistically quantified propositions, which is presumably the most straightforward and intuitively appealing, using the classic minimum operation and mentioning other t-norms. We also outline the use of the Sugeno and Choquet integrals proposed in our previous papers. We show an application to the absolute performance type analysis of time series data on daily quotations of an investment fund over an 8-year period, by presenting first an analysis of characteristic features of quotations, under various (degrees of) granulations assumed, and then by listing some more interesting and useful summaries obtained. We propose a convenient presentation of linguistic summaries focused on some characteristic feature exemplified by what happens "almost always," "very often," "quite often," "almost never," etc. All these analyses are meant to provide means to support a human user to make decisions. C

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Association Networks in Time Series Data Mining

Cited by 9 publications

References 5 publications

Are Foreign Currency Markets Interdependent? Evidence from Data Mining Technologies

Are Foreign Currency Markets Interdependent? Evidence from Data Mining Technologies

On Trend Association Analysis of Time Series of Atmospheric Pollutants and Meteorological Variables in Mexico City Metropolitan Area

An approach to the linguistic summarization of time series using a fuzzy quantifier driven aggregation

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