Application of fuzzy time series models for forecasting the amount of Taiwan export

Wong, Hsien-Lun; Tu, Yi-Hsien; Wang, Chi-Chen

doi:10.1016/j.eswa.2009.06.106

Cited by 43 publications

(25 citation statements)

References 15 publications

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“…FTS is used in various areas such as forecasting electricity load demand [4], stock exchange [5][6][7][8][9][10], rainfall and temperature forecasting [11], pollution [12], enrollments [13][14][15], etc. There are two major categories of FTS algorithms: FTS algorithms based on intervals of the universal set [3,16] and FTS algorithms based on fuzzy clustering [17][18][19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

A clustering based forecasting algorithm for multivariable fuzzy time series using linear combinations of independent variables

Askari

Montazerin

Zarandi

2015

Applied Soft Computing

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Section: Introductionmentioning

confidence: 99%

A clustering based forecasting algorithm for multivariable fuzzy time series using linear combinations of independent variables

Askari

Montazerin

Zarandi

2015

Applied Soft Computing

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“…Hsien-Lun Wong et al in [11] claimed that for short term period, time fuzzy time series performs better for prediction. They used fuzzy time series employing ARIMA and vector ARMA model for prediction.…”

Section: Literature Reviewmentioning

confidence: 99%

Using Machine Learning Classifiers to Predict Stock Exchange Index

Ghazanfar¹,

Alahmari²,

Aldhafiri³

et al. 2017

IJMLC

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Abstract-Predicting stock exchange index is an attractive research topic in the field of machine learning. Numerous studies have been conducted using various techniques to predict stock market volume. This paper presents first detailed study on data of Karachi Stock Exchange (KSE) and Saudi Stock Exchange (SSE) to predict the stock market volume of ten different companies. In this study, we have applied and compared salient machine learning algorithms to predict stock exchange volume. The performance of these algorithms have been compared using accuracy metrics on the dataset, collected over the period of six months, by crawling the KSE and SSE website.Index Terms-Stock exchange prediction, machine learning, SVM, neural networks, Bayesian network, Ada-boost.

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“…5 (1) Group 3 3 → 5 (1) 6 → 7 (1), 9 (1) Group 4 5 → 9 (1) 8 → 8 (1), 9 (1), 11 (1) Group 5 7 → 7 (1), 11 (1) 8 → 10 (1), 11 (1) Group 6 8 → 10 (1) 10 → 7 (1), 14 (1) Group 7 9 → 7 (1), 8 (1), 9 (1) 12 → 9 (1), 15 (1) Group 8 10 → 11 (1) 14 → 13 (1), 17 (1) Group 9 11 → 14 (1), 15 (1) 15 21 (1)…”

Section: Proposed Modelmentioning

confidence: 99%

“…In the literatures, the experimental analysis existing in many areas, such as forecasting stock price [4][5], tourism demand[6], temperature [7], amount of export [8] and dry bulk shipping index [9], etc.Most of the exiting methods mainly focused on partitioning the universe of discourse, constructing fuzzy relationships from the fuzzy set, forecasting and defuzzifing the forecasting output. A proper choice of the length of each interval can greatly improve the forecasting results.…”

mentioning

confidence: 99%

Generalized Fuzzy Time Series Forecasting Model Enhanced with Particle Swarm Optimization

Qiu¹,

Zhang²,

Zhang³

2015

IJUNESST

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1.IntroductionSince fuzzy time series model was proposed by Song and Chissom [1][2][3] in 1993, there are many forecasting models have been developed to deal with the forecasting problems due to their capability of dealing with the uncertainty and vagueness inherent in the data collected. In the literatures, the experimental analysis existing in many areas, such as forecasting stock price [4][5], tourism demand[6], temperature [7], amount of export [8] and dry bulk shipping index [9], etc.Most of the exiting methods mainly focused on partitioning the universe of discourse, constructing fuzzy relationships from the fuzzy set, forecasting and defuzzifing the forecasting output. A proper choice of the length of each interval can greatly improve the forecasting results. Huarng [10,11] presented some methods to obtain the proper lengths of the intervals for the fuzzy time series model. Chen proposed the automatic clustering technique to generate clustering-based intervals for some fuzzy time series forecasting models [12,13].Literatures [14,15]introduced an approach which using an optimization technique with a single-variable constraint to determine an optimal interval length. The particle swarm optimization algorithm and some other algorithms were used to search the appropriate intervals on the fuzzy time series [16][17][18][19] as well geneticalgorithms [20]. To improve the fuzzy time series model in constructing fuzzy relationships, forecasting and

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Application of fuzzy time series models for forecasting the amount of Taiwan export

Cited by 43 publications

References 15 publications

A clustering based forecasting algorithm for multivariable fuzzy time series using linear combinations of independent variables

A clustering based forecasting algorithm for multivariable fuzzy time series using linear combinations of independent variables

Using Machine Learning Classifiers to Predict Stock Exchange Index

Generalized Fuzzy Time Series Forecasting Model Enhanced with Particle Swarm Optimization

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