A Type 2 fuzzy time series model for stock index forecasting

Huarng, Kun‐Huang; Yu, Tiffany Hui‐Kuang

doi:10.1016/j.physa.2004.11.070

Cited by 198 publications

(95 citation statements)

References 29 publications

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“…In fact, both problems are typically affected by uncertainty at different levels: in the problem definition (e.g., constraints) or by considering the output (e.g., decision variables). It is worth citing the use of higher-order fuzzy constructs also in time series analysis (Chen and Tanuwijaya 2011;Huarng and Yu 2005), where either the time and amplitude (e.g., the time series realizations) domains are subjected to proper granulations. Finally, rough set theory found considerable application in many data analysis contexts.…”

Section: Granular Computing As a General Data Analysis Frameworkmentioning

confidence: 99%

Granular computing, computational intelligence, and the analysis of non-geometric input spaces

Livi

Sadeghian

2015

Granul. Comput.

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Data granulation emerged as an important paradigm in modeling and computing with uncertainty, exploiting information granules as the main mathematical constructs involved in the context of granular computing. In this paper, we comment on the importance of data granulation in computational intelligence methods. Toward this aim, we discuss also the peculiar aspects related to the analysis of non-geometric patterns, which have recently attracted considerable attention by researchers. As a conclusion, we elaborate over the fundamental, conceptual problems underlying the process of data granulation, which drive the quest for a sound theory of granular computing.

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Section: Granular Computing As a General Data Analysis Frameworkmentioning

confidence: 99%

Granular computing, computational intelligence, and the analysis of non-geometric input spaces

Livi

Sadeghian

2015

Granul. Comput.

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“…However, with respect to forecasting proposals most authors follows the scheme originally suggested by Song and Chissom. In our context, fuzzy approaches for solving time series problems have been applied in stock indices forecasting and for modeling business cycles (see, for instance, [12,13,14,15,16]). While forecasting returns on a given portfolio has not been treated in previous researches.…”

Section: Introductionmentioning

confidence: 99%

Comparative analysis of forecasting portfolio returns using Soft Computing technologies

Rubio-Manzano¹,

Bermúdez²,

Vercher³

2015

Proceedings of the 2015 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and

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We propose using fuzzy time series (FTS) to forecast the future performance of returns on portfolios. We model the portfolio selection problem by means of possibilistic moments, and approximate the uncertainty of the return on a given portfolio by trapezoidal fuzzy numbers.Some modifications into the classical models of FTS, based on weighted operators, allow us generating trapezoidal numbers as forecasters of the performance of portfolio returns. We incorporate our proposals into classical FTS methods and analyze their effectiveness with respect to FTS models with a possibilistic interval-valued mean approach, using historical returns on assets from the Spanish stock market.

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“…The importance of further developments in soft computing led to several papers devoted to forecasting stock indexes using techniques such as support vector machines (e.g., Chiu & Chen, 2009;Huang, Nakamori, & Wang, 2005;Kim, 2003;Pai & Lin, 2005;Wen et al, 2010), fuzzy systems (e.g., Chang & Liu, 2008;Chang, Wang, & Liu, 2007;Huang & Yu, 2005;Wang, 2003), genetic algorithms (e.g., Chen et al, 2009;Oh, Kim, & Min, 2005;Oh, Kim, Min, & Lee, 2006;Potvin, Soriano, & Vallee, 2004) and mixed methods (e.g., Armano, Marchesi, & Murru, 2005;Armano, Murru, & Roli, 2002;Hassan, Nath, & Kirley, 2007;Kwon & Moon, 2007;Leigh, Purvis, & Ragusa, 2002).…”

Section: Introductionmentioning

confidence: 99%

CAST: Using neural networks to improve trading systems based on technical analysis by means of the RSI financial indicator

Rodríguez-González

García-Crespo

Colomo‐Palacios

et al. 2011

Expert Systems with Applications

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a b s t r a c tStock price predictions have been a field of study from several points of view including, among others, artificial intelligence and expert systems. For short term predictions, the technical indicator relative strength indicator (RSI) has been published in many papers and used worldwide.CAST is presented in this paper. CAST can be seen as a set of solutions for calculating the RSI using arti ficial intelligence techniques. The improvement is based on the use of feedforward neural networks to calculate the RSI in a more accurate way, which we call the iRSI. This new tool will be used in two sce narios. In the first, it will predict a market in our case, the Spanish IBEX 35 stock market. In the second, it will predict single company values pertaining to the IBEX 35. The results are very encouraging and reveal that the CAST can predict the given market as a whole along with individual stock pertaining to the IBEX 35 index.

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A Type 2 fuzzy time series model for stock index forecasting

Cited by 198 publications

References 29 publications

Granular computing, computational intelligence, and the analysis of non-geometric input spaces

Granular computing, computational intelligence, and the analysis of non-geometric input spaces

Comparative analysis of forecasting portfolio returns using Soft Computing technologies

CAST: Using neural networks to improve trading systems based on technical analysis by means of the RSI financial indicator

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