Disaster Forecasting Approach in Indonesia: A Fuzzy Time Series - Markov Chain Model

A Hidden Markov Model-based Forecasting Model for Fuzzy Time Series

Vague and incomplete data represented as linguistic values massively exists in diverse real-word applications. The task of forecasting fuzzy time series under uncertain circumstances is thus of great important but difficult. The inherent uncertainty involving time evolution usually makes the transition of states in a system probabilistic. In this paper, we proposed a new forecasting model based...

Time Variant Fuzzy Time Series Approach for Forecasting Using Particle Swarm Optimization

  Fuzzy time series have been developed during the last decade to improve the forecast accuracy. Many algorithms have been applied in this approach of forecasting such as high order time invariant fuzzy time series. In this paper, we present a hybrid algorithm to deal with the forecasting problem based on time variant fuzzy time series and particle swarm optimization algorithm, as a highly effi...

A Fuzzy-LP Approach in Time Series Forecasting

Financial time series forecasting with a bio-inspired fuzzy model

0957-4174/$ see front matter 2012 Elsevier Ltd. A http://dx.doi.org/10.1016/j.eswa.2012.02.135 ⇑ Corresponding author. E-mail address: jose-luis.aznarte@mines-paristech In general, times series forecasting is considered as a highly complex problem, which is particularly true for financial time series. In this paper, a fuzzy model evolved through a bio-inspired algorithm is proposed to produce a...

A novel two-factor forecasting model for fuzzy time series

The study of fuzzy time series has increasingly attracted much attention due to its salient capabilities of tackling vague and incomplete data. A variety of forecasting models have devoted to improving forecasting accuracy, however, the issue of partitioning intervals has rarely been investigated. Recently, we proposed a novel deterministic forecasting model to eliminate the major overhead of d...