The generation of qualitative descriptions of multivariate time series using fuzzy logic

2014 ◽  
Vol 23 ◽  
pp. 546-555 ◽  
Author(s):  
Juan Moreno-Garcia ◽  
Luis Rodriguez-Benitez ◽  
Juan Giralt ◽  
Ester del Castillo
Keyword(s):  
Time Series ◽  
Fuzzy Logic ◽  
Multivariate Time Series

scholarly journals A New Fuzzy Logic-Based Similarity Measure Applied to Large Gap Imputation for Uncorrelated Multivariate Time Series

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Thi-Thu-Hong Phan ◽  
André Bigand ◽  
Émilie Poisson Caillault
Keyword(s):  
Time Series ◽  
Fuzzy Logic ◽  
Similarity Measure ◽  
Missing Values ◽  
Multivariate Time Series ◽  
Similarity Measures ◽  
New Approach ◽  
The One ◽  
High Level ◽  
Basic Similarity

The completion of missing values is a prevalent problem in many domains of pattern recognition and signal processing. Analyzing data with incompleteness may lead to a loss of power and unreliable results, especially for large missing subsequence(s). Therefore, this paper aims to introduce a new approach for filling successive missing values in low/uncorrelated multivariate time series which allows managing a high level of uncertainty. In this way, we propose using a novel fuzzy weighting-based similarity measure. The proposed method involves three main steps. Firstly, for each incomplete signal, the data before a gap and the data after this gap are considered as two separated reference time series with their respective query windowsQbandQa. We then find the most similar subsequence (Qbs) to the subsequence before this gapQband the most similar one (Qas) to the subsequence after the gapQa. To find these similar windows, we build a new similarity measure based on fuzzy grades of basic similarity measures and on fuzzy logic rules. Finally, we fill in the gap with average values of the window followingQbsand the one precedingQas. The experimental results have demonstrated that the proposed approach outperforms the state-of-the-art methods in case of multivariate time series having low/noncorrelated data but effective information on each signal.

Penerapan Logika Samar dalam Peramalan Data Runtun Waktu

2011 ◽  
Vol 3 (2) ◽  
pp. 11-15
Author(s):  
Seng Hansun
Keyword(s):  
Time Series ◽  
Fuzzy Logic ◽  
Time Series Analysis ◽  
Fuzzy Sets ◽  
Fuzzy System ◽  
Time Series Data ◽  
Fuzzy Logic System ◽  
Series Data ◽  
Series Analysis ◽  
Logic System

Recently, there are so many soft computing methods been used in time series analysis. One of these methods is fuzzy logic system. In this paper, we will try to implement fuzzy logic system to predict a non-stationary time series data. The data we use here is Mackey-Glass chaotic time series. We also use MATLAB software to predict the time series data, which have been divided into four groups of input-output pairs. These groups then will be used as the input variables of the fuzzy logic system. There are two scenarios been used in this paper, first is by using seven fuzzy sets, and second is by using fifteen fuzzy sets. The result shows that the fuzzy system with fifteen fuzzy sets give a better forecasting result than the fuzzy system with seven fuzzy sets. Index Terms—forecasting, fuzzy logic, Mackey-Glass chaotic, MATLAB, time series analysis

Recognition of anomalies from time series by fuzzy logic methods

2008 ◽  
Vol 10 (1) ◽  
pp. 1-9 ◽  
Author(s):  
A. D. Gvishiani ◽  
S. M. Agayan ◽  
Sh. R. Bogoutdinov ◽  
E. M. Graeva ◽  
J. Zlotnicki ◽  
...  
Keyword(s):  
Time Series ◽  
Fuzzy Logic

A contribution to the identification in fuzzy-logic control

1990 ◽  
Vol 55 (4) ◽  
pp. 951-963 ◽  
Author(s):  
Josef Vrba ◽  
Ywetta Purová
Keyword(s):  
Time Series ◽  
Fuzzy Logic ◽  
Fuzzy Logic Control ◽  
Fuzzy Logic Controller ◽  
Automatic Generation ◽  
Linguistic Variable ◽  
Fuzzy Subset ◽  
Input Output ◽  
Logic Control ◽  
Correlation Tables

A linguistic identification of a system controlled by a fuzzy-logic controller is presented. The information about the behaviour of the system, concentrated in time-series, is analyzed from the point of its description by linguistic variable and fuzzy subset as its quantifier. The partial input/output relation and its strength is expressed by a sort of correlation tables and coefficients. The principles of automatic generation of model statements are presented as well.

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