Viktor Pavliska

Analysis of seasonal time series using fuzzy approach

A new methodology for the analysis and forecasting of time series is proposed. It directly employs two soft computing techniques: the fuzzy transform and the perception-based logical deduction. Thanks to the use of both these methods, and to the innovative approach, consisting of the construction of several independent models, the methodology is successfully applicable to robust long-time predictions.

Analysis and prediction of time series using fuzzy transform

A new methodology for forecasting of time series is proposed. It is based on combination of two techniques: fuzzy transform and perception-based logical deduction on the basis of learned linguistic description.

A linguistic approach to time series modeling with the help of F-transform

In this paper, we describe an approach to modeling and forecasting time series that uses the theory of evaluative linguistic expressions, fuzzy/linguistic IF-THEN rules and the fuzzy transform method. We show that the use of a linguistic approach allows better readability and understandability without any significant deterioration in precision.

The concept of LFLC 2000: its specificity, realization and power of applications

Linguistic Fuzzy Logic Controller (LFLC) 2000 is a complex tool for the design of linguistic descriptions and fuzzy control based on these descriptions. Unique methodology and theoretical results upon which is LFLC 2000 based are presented. Then, main purposes of it are sketched and some implementation aspects are discussed. Presentation of existing and perspective applications concludes the paper.

F-transform and Fuzzy Natural logic in Time Series Analysis

This paper continues the development of the innovative method for time series analysis and forecasting using special soft-computing techniques: fuzzy (F-) transform and Fuzzy Natural Logic. We will demonstrate that the F-transform is a proper technique for extraction of the trend-cycle of time series. Furthermore, we will elaborate in more detail automatic generation of linguistic evaluation of its behavior in arbitrary time slots. Thanks to the firstdegree F-transform (F1-transform), this works even if the graph of the time series visually does not suggest a clear tendency.

Parallel mining of fuzzy association rules on dense data sets

The aim of this paper is to present a scalable parallel algorithm for fuzzy association rules mining that is suitable for dense data sets. Unlike most of other approaches, we have based the algorithm on the Webbs OPUS search algorithm [1]. Having adopted the master/slave architecture, we propose a simple recursion threshold technique to allow load-balancing for high scalability.

Time Series Trend Extraction and Its Linguistic Evaluation Using F-Transform and Fuzzy Natural Logic

In this chapter, we contribute to the innovative method of time series analysis and forecasting using fuzzy transform and fuzzy natural logic. We will demonstrate that the F-transform is a powerful technique for extraction of the trend-cycle of time series. Further step is automatic linguistic evaluation of the course (tendency) of time series in a specified time slot. The main tool is the first degree F-transform (F1-transform) which makes it possible to estimate average tangent of the given function. We thus obtain an objective result even if the trend is not visually apparent from the graph of the time series.

Linguistic approach to time series analysis and forecasts

Linguistic approach of time series analysis is suggested. It adopts aspects of the decomposition and autoregression. The linguistic, i.e., interpretable and transparent, nature of the approach is emphasized. Precision of the suggested approach is demonstrated on real time series.

Undefined Values in Fuzzy Logic

The main objective of this paper is to propose an extended algebra of truth values by special truth values which may have several interpretations, such as “undefined”, “non-applicative” “overdetermined”, “undetermined”, etc. In this paper, we will analyze several situations, where the non-existent data may come from, and show within a fuzzy sets framework that different cases of non-existence have to be carefully treated and interpreted in a different way.

Fuzzy Association Rules on Data with Undefined Values

Handling of missing values is a very common in data processing. However, data values may be missing not only because of lack of information, but also because of undefinedness (such as asking for the age of non-married person’s spouse). The aim of this paper is to propose an extension of fuzzy association rules framework for data with undefined values.