Weighted fuzzy time series models for TAIEX forecasting

By Hui-Kuang Yu. 2005

503 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/e4e11f7a1519c4b167b5a6555aef6255a47bb336

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In Economics and Econometrics

In Management Science and Operations Research

Citations in the corpus

A HYBRID RECURRENT NEURAL NETWORKS MODEL BASED ON SYNTHESIS FEATURES TO FORECAST THE TAIWAN STOCK MARKET (2012, methodology): “A 10- month period of the stock data, from January to October, is defined as training data, and the remaining period, from November and December, is used for testing (Chen 1996, Yu 2005).”
Application of type-2 neuro-fuzzy modeling in stock price prediction (2012, methodology): “The given training data set is partitioned into clusters through input-similarity and output-similarity tests, and a type-2 TSK rule is derived from each cluster to form a fuzzy rule base.”
Application of data mining techniques in stock markets: A survey (2010, methodology): “But their method requires extra observations to form type-2 fuzzy relations for each observation and thus require larger number of data than the conventional type-I methods (Huarng and Yu, 2005).”
A distance-based fuzzy time series model for exchange rates forecasting (2009, background): “Yu (2005) proposed to assign different weights on fuzzy logic relationships (FLRs) based on their recency.”
Improvement of Auto-Regressive Integrated Moving Average models using Fuzzy logic and Artificial Neural Networks (ANNs) (2009, methodology): “In addition, although the proposed model is basically designed for interval forecasting, its performance in point estimation is also more satisfactory than that of ARIMA, ANNs and fuzzy time series models such as Chen’s fuzzy time series (ﬁrst and second order) [8,9] and Yu’s fuzzy time series [62].”
Digital currency forecasting with chaotic meta-heuristic bio-inspired signal processing techniques (2019, methodology): “It is expressed that the hybrid model has a higher performance when compared to Chen's model [25], Yu's model [26], auto-regressive (AR),”