Linguistic Summarization Of Time Series Research Articles

A possibilistic approach for interval type-2 fuzzy linguistic summarization of time series

Linguistic summarization with fuzzy sets, extracting explicit and concise knowledge from data, has received a great attention because of its compatibility with human cognitive mechanism. The most crucial step in linguistic summarization is certainly the evaluation of linguistic summaries. In this study, a new fuzzy cardinality based possibilistic approach is proposed for evaluating linguistic summaries in interval type-2 fuzzy environment. It is proved that the proposed possibilistic approach satisfies the desired properties defined in the literature. An application of the proposed possibilistic approach is presented on financial time series including the three stocks in Borsa Istanbul (BIST) covering the period for the last 10 years. It is noticed that the proposed possibilistic approach generates more stable results than the probabilistic approach of Boran and Akay (IEEE Trans Cybern 44:1632–1645, 2014).

Fuzzy logic‐based linguistic summaries of time series: a powerful tool for discovering knowledge on time varying processes and systems under imprecision

We present a novel approach to linguistic data summarization for numeric data, i.e., of the numbers to text type. Linguistic summarization is meant as a process of a comprehensive description of big and complex datasets via short statements in natural language. First, we briefly survey main developments in the traditional, well developed, and powerful approach to linguistic data summarization based on tools and techniques of natural language generation (NLG), notably due to Reiter and his collaborators. We indicate that this approach has a serious limitation on the representation and processing of imprecision that is characteristic for natural languages. We show that a fuzzy logic‐based approach to linguistic data summarization can be a simple yet efficient solution in this respect. We present the linguistic summaries represented by protoforms in the form of linguistically quantified propositions dealt with using tools and techniques of fuzzy logic to grasp an inherent imprecision of natural language. Such linguistic data summaries can provide a human user, whose only natural means of articulation and communication is natural language, with a simple yet effective and efficient means for the representation and manipulation of knowledge about processes and systems. We concentrate on the linguistic summarization of dynamic processes and systems, dealing with data represented as time series. We extend the basic, static data‐oriented concept of a linguistic data summary to the case of time series data, present various possible protoforms of linguistic summaries, and an analysis of their properties and ways of generation. We show two our own real applications of the new tools of linguistic summarization of time series, for the summarization of quotations of an investment (mutual) fund, and of Web server logs, to show the power of the tool. We also mention some other applications known from the literature. We conclude with some remarks on the strength of the linguistic summarization for broadly perceived data mining and knowledge discovery and some possible further research directions. WIREs Data Mining Knowl Discov 2016, 6:37–46. doi: 10.1002/widm.1175This article is categorized under: Fundamental Concepts of Data and Knowledge > Human Centricity and User Interaction Technologies > Computational Intelligence Technologies > Machine Learning

Linguistic Summarization of Europe Brent Spot Price Time Series Along with the Interpretations from the Perspective of Turkey

Compared to the predictive models for forecasting crude oil price, the descriptive models are also important for discovering the relations among factors that have influence on crude oil price. In this paper, linguistic summarization of time series, a descriptive technique, is used to summarize the Europe Brent Spot Price EBSP data dollars per barrel from May 1987 to July 2013 on daily and monthly base. This study is the first attempt in the literature for linguistic summarization of the EBSP time series with the aim to establish a decision support tool that can be used for generating linguistic propositions helping the estimation of future behaviors and fluctuations of oil price time series for oil-importer countries such as Turkey. Economic interpretations of generated linguistic propositions about the main features of EBSP trends duration, dynamics of change, and variability and a procurement strategy, indicating that Turkey could cut back on oil importation about $573.88 million between 1996 and 2012, are suggested from the perspective of Turkey.

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

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. © 2010 Wiley Periodicals, Inc.

Linguistic summarization of time series using a fuzzy quantifier driven aggregation

We propose new types of linguistic summaries of time-series data that extend those proposed in our previous papers. The proposed summaries of time series refer to the summaries of trends identified here with straight line segments of a piecewise linear approximation of time series. We first show how to construct such an approximation. Then we employ a set of features (attributes) to characterize the trends such as the slope of the line segment, the goodness of approximation and the length of the trend. The derivation of a linguistic summary of a time series is then related to a linguistic quantifier driven aggregation of trends. For this purpose we employ the classic Zadeh's calculus of linguistically quantified propositions but, extending our previous works, with different t-norms in addition to the basic minimum. We show an application to the analysis of time-series data on daily quotations of an investment fund over an eight year period, present some interesting linguistic summaries obtained, and show results for different t-norms. The results are very promising.