A novel computation for predicting time series using fuzzy logical distance connectivity function and visibility graph theory
| dc.citation.epage | 21 | |
| dc.citation.issue | 1 | |
| dc.citation.spage | 14 | |
| dc.contributor.affiliation | Iнженерний коледж Крiшни | |
| dc.contributor.affiliation | Iнститут технологiй та управлiння Г. Л. Баджая | |
| dc.contributor.affiliation | Krishna Engineering College | |
| dc.contributor.affiliation | GL Bajaj Institute of Technology and Management | |
| dc.contributor.author | Такур, Ганеш Кумар | |
| dc.contributor.author | Прия, Бандана | |
| dc.contributor.author | Thakur, Ganesh Kumar | |
| dc.contributor.author | Priya, Bandana | |
| dc.date.accessioned | 2023-03-06T12:28:18Z | |
| dc.date.available | 2023-03-06T12:28:18Z | |
| dc.date.created | 2020-01-01 | |
| dc.date.issued | 2020-01-01 | |
| dc.description.abstract | Граф видимостi є набором мiсцерозташувань, якi лежать на лiнiї, i може бути iнтерпретований як графо-теоретичне подання часового ряду, в той час як нечiткий граф говорить про зв’язок мiж лiнiями, точно демонструючи рiвень зв’язку мiж об’єктами заданого набору. Багато графiв не показують правильнi минулi значення. Навiть знаючи минулi значення часового ряду, прогнозування майбутнiх значень не може бути точним. Так, щоб точно знайти справжнi значення, у цiй статтi введено граф видимостi за значеннями часових рядiв (xt, yt),(xu, yu) разом зi значеннями нечiтких вузлiв f1, f2, . . . , f. Розгляд нечiткої логiки з подiбнiстю вузлiв у минулому не дає бiльш точного прогнозу, оскiльки схожiсть вузлiв мiстить тiльки значення минулих вузлiв. Отже, основна мета цiєї статтi — це запропонувати розрахунок для прогнозування бiльш точної стратегiї вимiрювання iнформацiї шляхом знаходження подiбностi всiх нечiтких вузлiв f1, f2, . . . , fn з їх функцiєю вiддалi fd(α) i функцiєю зв’язностi α. Результат обчислювань Y(x+1) демонструватиме точнiшi значення часових рядiв. | |
| dc.description.abstract | The visibility graph is a set of locations that lie in a line that can be interpreted as a graph-theoretical representation of a time series, while the fuzzy graph speaks about the connection between the lines by accurately demonstrating the level of the connection between the objects of a given set. Many graphs do not show proper previous values. Even knowing the previous values of times series, prediction of the future values will not be accurate. Therefore, to find the real values exactly, this paper introduces the Visibility graph by time series values (xt, yt),(xu, yu) along with the Fuzzy node values f1, f2, . . . , fn. Considering the past nodes by Fuzzy logic, similarity does not give a more accurate prediction because the nodes similarity contains the past node values only. The fundamental target of this paper is to propose a calculation to predict a more exact strategy to measure information by finding the similarities of all fuzzy nodes f1, f2, . . . , fn with their distance function fd(α) and the connectivity function α. The results of the computational outcome Y(x+1) will demonstrate more accurate values of time series. | |
| dc.format.extent | 14-21 | |
| dc.format.pages | 8 | |
| dc.identifier.citation | Thakur G. K. A novel computation for predicting time series using fuzzy logical distance connectivity function and visibility graph theory / Ganesh Kumar Thakur, Bandana Priya // Mathematical Modeling and Computing. — Lviv : Lviv Politechnic Publishing House, 2020. — Vol 7. — No 1. — P. 14–21. | |
| dc.identifier.citationen | Thakur G. K., Priya B. (2020) A novel computation for predicting time series using fuzzy logical distance connectivity function and visibility graph theory. Mathematical Modeling and Computing (Lviv), vol. 7, no 1, pp. 14-21. | |
| dc.identifier.doi | DOI: 10.23939/mmc2020.01.014 | |
| dc.identifier.uri | https://ena.lpnu.ua/handle/ntb/57514 | |
| dc.language.iso | en | |
| dc.publisher | Видавництво Львівської політехніки | |
| dc.publisher | Lviv Politechnic Publishing House | |
| dc.relation.ispartof | Mathematical Modeling and Computing, 1 (7), 2020 | |
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| dc.relation.referencesen | [3] Box G. E., Jenkins G. M., Reinsel G. C., Ljung G. M. Time series analysis: Forecasting and Control. John Wiley & Sons, Hoboken (2015). | |
| dc.relation.referencesen | [4] Chliamovitch G., Dupuis A., Golub A., Chopard B. Improving predictability of time series using Maximum entropy methods. EPL. 110 (1), 10003 (2015). | |
| dc.relation.referencesen | [5] Hyndman R. J., Khandakar Y. Automatic time series forecasting: The forecast package for R. 27 (3) (2018). | |
| dc.relation.referencesen | [6] Kaya B., Poyraz M. Age-series based link prediction in evolving disease networks. Computers in Biology and Medicine. 63, 1–10 (2015). | |
| dc.relation.referencesen | [7] Wang P., Xu B., Wu Y., Zhou X. Link prediction in social networks: the state-of-the-art. Science China Information Sciences. 58 (1), 1–38 (2015). | |
| dc.relation.referencesen | [8] Zhang R., Ran X., Wang C., Deng Y. Fuzzy evaluation of network vulnerability. Qual. Reliab. Engng. Int.32 (5), 1715–1730 (2016). | |
| dc.relation.referencesen | [9] Iswanto I., Wahyunggoro O., Cahyadi A. I. Path Planning Based on Fuzzy Decision Trees and Potential Field. International Journal of Electrical and Computer Engineering. 6 (1), 212–222 (2016). | |
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| dc.relation.referencesen | [13] Broumi S., Smarandache F., Talea M., Bakali A. An introduction to bipolar single valued neutrosophic graph theory. Applied Mechanics and Materials. 841, 184–191 (2016). | |
| dc.relation.referencesen | [14] Anbalagan T., Maheswari S. U. Classification and prediction of stock market index based on fuzzy meta graph. Procedia Computer Science. 47, 214–221 (2015). | |
| dc.relation.referencesen | [15] Cai Q., Zhang D., Zheng W., Leung S. C. H. A new fuzzy time series forecasting model combined with ant colony optimization and auto-regression. Knowledge–Based Systems. 74, 61–68 (2015). | |
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| dc.relation.referencesen | [17] Sun B., Guo H., Karimi H. R., Ge Y., Xiong S. Prediction of stock index futures prices based on fuzzy sets and multivariate fuzzy time series. Neurocomputing. 151 (3), 1528–1536 (2015). | |
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| dc.relation.referencesen | [19] Stephen M., Gu C., Yang H. Visibility graph based time series analysis. PloS ONE. 10 (11), e0143015(2015). | |
| dc.relation.referencesen | [20] Montgomery D. C., Cheryl L. J., Kulahci M. Introduction to time series analysis and forecasting. John Wiley & Sons (2015). | |
| dc.relation.referencesen | [21] Gao Z.-K., Small M., Kurths J. Complex network analysis of time series. EPL. 116 (5), 50001 (2017). | |
| dc.relation.referencesen | [22] Jiang W., Wei B., Zhan J., Xie C., Zhou D. A visibility graph power averaging aggregation operator: A methodology based on network analysis. Computers & Industrial Engineering. 101, 260–268 (2016). | |
| dc.relation.referencesen | [23] Gao Z.-K., Yang Y.-X., Fang P.-C., Zou Y., Xia C.-Y., Du M. Multi scale complex network for analyzing experimental multivariate time series. EPL. 109 (3), 30005 (2015). | |
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| dc.relation.referencesen | [25] Chiancone A., Franzoni V., Li Y., Markov K., Milani A. Leveraging Zero Tail in Neighbourhood for Link Prediction. 2015 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT). 3, 135–139 (2015). | |
| dc.relation.referencesen | [26] Iswanto I., Wahyunggoro O., Cahyadi A. I. Quadrotor Path Planning Based On Modified Fuzzy Cell Decomposition Algorithm. TELKOMNIKA (Telecommunication Computing Electronics and Control). 14(2), 655–664 (2016). | |
| dc.rights.holder | ©2020 Lviv Polytechnic National University CMM IAPMM NASU | |
| dc.subject | часовi ряди | |
| dc.subject | нечiтка логiка | |
| dc.subject | нечiтка вiдстань | |
| dc.subject | функцiя зв’язностi | |
| dc.subject | граф видимостi | |
| dc.subject | time series | |
| dc.subject | fuzzy logic | |
| dc.subject | fuzzy distance | |
| dc.subject | connectivity function | |
| dc.subject | visibility graph | |
| dc.subject.udc | 03B52 | |
| dc.subject.udc | 05C72 | |
| dc.title | A novel computation for predicting time series using fuzzy logical distance connectivity function and visibility graph theory | |
| dc.title.alternative | Нове обчислення для прогнозування часових рядів з використанням нечіткої логічної функції з’єднання відстані та теорії графів видимості | |
| dc.type | Article |