Perception Based Patterns in Time Series Data Mining

Perception-Based Functions in Qualitative Forecasting

“…In comparison with methods of granulation of time series shape patterns falling less steeply, rising more steeply, etc., considered in [1,6], the methods of granulation of convex-concave patterns considered in this chapter give the possibility to modify any linear function defined on any interval. Such flexibility of these methods gives them an advantage not only in modeling perception-based functions but also in modeling and segmentation of time series.…”

On some types of linguistic summaries of time series

“…with circulation using surface and satellite data: 1955-2001. In Theoretical and Applied Climatology 79 (3)(4):185-208.…”

“…young) as in, e.g., T(most of young employees earn low salary) = 0.9 (3) Thus, basically, the core of a linguistic summary is a linguistically quantified proposition in the sense of Zadeh [21]. A linguistically quantified proposition, corresponding to (2) may be written as Qy's are S (4) and the one corresponding to (3) may be written as R trends that took Q time are S (9) and exemplified by:…”

“…Recently many methods of trend analysis have been introduced (e.g., [3], [5], [7], [15], [16]). Most of them are based on the detection of change of the first and second derivative.…”

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

“…There have been a numerous studies reported on linguistic summarization based upon fuzzy sets with different approaches applied to various areas: linguistic summarization of time series, decision support systems, text categorization, reconciliation processes, human motion analysis, social networks, monitoring of internet traffic flows, fall detection, financial reports, driving and the traffic activities . Undoubtedly, one of the most important application areas of linguistic summarization is time series data mining (TSDM) with generating propositions describing trends of time series in terms of the following three aspects: duration, variability, and dynamics of change . For instance, “trends with low variability that took most of the time” is a proposition that can be obtained by linguistic summarization.…”