Dự Báo Chuỗi Thời Gian Mờ Dựa Trên Ngữ Nghĩa

Hiếu, Nguyễn Duy; Lân, Vũ Như; Hồ, Nguyễn Cát

doi:10.15625/vap.2015.000156

Cited by 1 publication

(1 citation statement)

References 9 publications

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“…Recently, there are some studies applying the HAs theory to the fuzzy time series forecasting problem [42,43,44,45] The main idea of these studies is only to apply the fuzziness intervals of words, interpreted as their interval-semantics, to decompose the universe of discourse into an interval-partition instead of determining these intervals based only on the researchers' intuition. The authors of studies [42,43,44] proposed a forecasting method based on HAs using semantization and desemantization transformations, which are success-fully applied in fuzzy control. They tried to determine an interval partition of historical data similarly as ordinary fuzzy time series forecasting methods and also made some modifications to improve forecasting accuracy, for instance, optimizing the selection of forecasting model parameters.…”

Section: Introductionmentioning

confidence: 99%

Enrollment Forecasting Based on Linguistic Time Series

Hieu

Hồ

Lân

2020

JCC

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Dealing with the time series forecasting problem attracts much attention from the fuzzy community. Many models and methods have been proposed in the literature since the publication of the study by Song and Chissom in 1993, in which they proposed fuzzy time series together with its fuzzy forecasting model for time series data and the fuzzy formalism to handle their uncertainty. Unfortunately, the proposed method to calculate this fuzzy model was very complex. Then, in 1996, Chen proposed an efficient method to reduce the computational complexity of the mentioned formalism. Hwang et al. in 1998 proposed a new fuzzy time series forecasting model, which deals with the variations of historical data instead of these historical data themselves. Though fuzzy sets are concepts inspired by fuzzy linguistic information, there is no formal bridge to connect the fuzzy sets and the inherent quantitative semantics of linguistic words. This study proposes the so-called linguistic time series, in which words with their own semantics are used instead of fuzzy sets. By this, forecasting linguistic logical relationships can be established based on the time series variations and this is clearly useful for human users. The effect of the proposed model is justified by applying the proposed model to forecast student enrollment historical data.

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