Dynamic optimization of fuzzy cognitive maps for time series forecasting

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F16%3A50005184" target="_blank" >RIV/62690094:18450/16:50005184 - isvavai.cz</a>

Výsledek na webu

<a href="http://www.sciencedirect.com/science/article/pii/S0950705116300752" target="_blank" >http://www.sciencedirect.com/science/article/pii/S0950705116300752</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.knosys.2016.04.023" target="_blank" >10.1016/j.knosys.2016.04.023</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Dynamic optimization of fuzzy cognitive maps for time series forecasting

Popis výsledku v původním jazyce

In this paper we propose a new approach to learning fuzzy cognitive maps (FCMs) as a predictive model for time series forecasting. The first contribution of this paper is the dynamic optimization of the FCM structure, i.e., we propose to select concepts involved in the FCM model before every prediction is made. In addition, the FCM transformation function together with the corresponding parameters are proposed to be optimized dynamically. Finally, the FCM weights are learned. In this way, the entire FCM model is learned in a completely new manner, i.e., it is continuously adapted to the current local characteristics of the forecasted time series. To optimize all of the aforementioned elements, we apply and compare 5 different population-based algorithms: genetic, particle swarm optimization, simulated annealing, artificial bee colony and differential evolution. For the evaluation of the proposed approach we use 11 publicly available data sets. The results of comparative experiments provide evidence that our approach offers a competitive forecasting method that outperforms many state-of-the-art forecasting models. We recommend to use our FCM-based approach for the forecasting of time series that are linear and tend to be trend stationary.

Název v anglickém jazyce

Dynamic optimization of fuzzy cognitive maps for time series forecasting

Popis výsledku anglicky

In this paper we propose a new approach to learning fuzzy cognitive maps (FCMs) as a predictive model for time series forecasting. The first contribution of this paper is the dynamic optimization of the FCM structure, i.e., we propose to select concepts involved in the FCM model before every prediction is made. In addition, the FCM transformation function together with the corresponding parameters are proposed to be optimized dynamically. Finally, the FCM weights are learned. In this way, the entire FCM model is learned in a completely new manner, i.e., it is continuously adapted to the current local characteristics of the forecasted time series. To optimize all of the aforementioned elements, we apply and compare 5 different population-based algorithms: genetic, particle swarm optimization, simulated annealing, artificial bee colony and differential evolution. For the evaluation of the proposed approach we use 11 publicly available data sets. The results of comparative experiments provide evidence that our approach offers a competitive forecasting method that outperforms many state-of-the-art forecasting models. We recommend to use our FCM-based approach for the forecasting of time series that are linear and tend to be trend stationary.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2016

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Knowledge-based systems

ISSN

0950-7051

e-ISSN

—

Svazek periodika

105

Číslo periodika v rámci svazku

August

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

9

Strana od-do

29-37

Kód UT WoS článku

000378961200003

EID výsledku v databázi Scopus

2-s2.0-84992304037