Fuzzy time series model based on red–black trees for stock index forecasting

Tavares, Thiago Henrique Barbosa de Carvalho; Ferreira, Bruno Pérez; Mendes, Eduardo M. A. M.

doi:10.1016/j.asoc.2022.109323

Cited by 7 publications

(3 citation statements)

References 25 publications

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“…Even though there is a variety of data structures that can assist in identifying the proper clusters from a fuzzy model’s divine of reasoning, de Carvalho Tavares, Ferreira & Mendes (2022) proposed a unique fuzzy model based on a red-black tree (RBT) data structure in order to enhance the prospects of achieving improved projections. The RBT data structure, supports more balance, enabling more certainty.…”

Section: Methodsmentioning

confidence: 99%

A systematic literature survey on recent trends in stock market prediction

Balasubramanian,

P.,

Badarudeen

et al. 2024

PeerJ Computer Science

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Prediction of the stock market is a challenging and time-consuming process. In recent times, various research analysts and organizations have used different tools and techniques to analyze and predict stock price movements. During the early days, investors mainly depend on technical indicators and fundamental parameters for short-term and long-term predictions, whereas nowadays many researchers started adopting artificial intelligence-based methodologies to predict stock price movements. In this article, an exhaustive literature study has been carried out to understand multiple techniques employed for prediction in the field of the financial market. As part of this study, more than hundreds of research articles focused on global indices and stock prices were collected and analyzed from multiple sources. Further, this study helps the researchers and investors to make a collective decision and choose the appropriate model for better profit and investment based on local and global market conditions.

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Section: Methodsmentioning

confidence: 99%

A systematic literature survey on recent trends in stock market prediction

Balasubramanian,

P.,

Badarudeen

et al. 2024

PeerJ Computer Science

View full text Add to dashboard Cite

show abstract

“…In 1993, Song and Chissom initiatively combined fuzzy set theory and time series analysis, and proposed fuzzy time series (FTS) forecasting model to deal with uncertainty and fuzzy linguistic variables issues in time series [1]. Since FTS model was proposed, it provides a novel and available idea for the research on time series prediction, and is widely used in economic indicator analysis [2][3][4], atmospheric environment [5][6][7], power sector [8][9][10] and other fields. In a nutshell, the FTS model proposed by Song and Chissom consists of four main steps: (1) Definition and partition of the universe of discourse, (2) Data fuzzification, (3) According to the sequence of training data, fuzzy logical relationships (FLRs) and fuzzy logical relationship groups (FLRGs) are established, (4) Defuzzification and prediction.…”

Section: Introductionmentioning

confidence: 99%

A Novel Multifactor Type-2 Fuzzy Time Series Model based on Improved Fuzzy C-means Algorithm and Justifiable Granularity for Stock Index Forecasting

Gong,

Feng

2023

Preprint

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Fuzzy time series (FTS) models have been widely used in prediction tasks due to their excellent ability to deal with uncertain and nonlinear problems. However, type-1 fuzzy set has limitations in dealing with uncertainty in some real-world problems, so an increasing number of intelligent systems adopt type-2 fuzzy set to better solve problems such as data noise and linguistic ambiguity. In this study, we propose a novel stock index forecasting model based on type-2 fuzzy set and a variable size interval partitioning technique. Firstly, constructing information granules with enhanced effectiveness and interpretability based on fuzzy C-means (FCM) clustering algorithm and principle of justifiable granularity to partition the universe of discourse. The FCM algorithm in this model obtains the optimal clustering result by adopting interval type-2 fuzzy approach to adjust the fuzzy parameter m in FCM. Secondly, in order to improve forecasting performance, three observations of stock index are modeled as type-2 fuzzy set to obtain a group of refined first-order fuzzy relationships. To evaluate the performance of this model, we implement experiments using six benchmark datasets collected from Taiwan Stock Exchange Index (TAIEX), and calculate the corresponding root mean square error (RMSE) as the evaluation criteria. Compared with nine representative time series forecasting approaches, experimental results show that our model can not only guarantee the effectiveness and robustness but also obtain enhanced prediction performance.

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“…The predictive model about fuzzy time series (FTS) has been applied to a variety of forecasting issues, including forecasts for Alabama university enrolment (S. M. Chen, 2002; S. M. Chen & Chung, 2006;Jeng et al, 2006;Song & Chissom, 1993;Tanuwijaya & Chen, 2009), stock price (M. Y. Chen & Chen, 2015; T. L. Chen et al, 2007;Cheng & Yang, 2018;Singh & Borah, 2014;Tavares et al, 2022), renewable energy (Çakır, 2023;Severiano et al, 2021), lumpy skin disease (Punyapornwithaya et al, 2023), temperature forecast (Lee et al, 2006), and portfolio returns forecast (Rubio et al, 2016), with the goal of lowering future uncertainty, also improving and streamlining planning.…”

mentioning

confidence: 99%

Ratio Interval-Frequency Density with Modifications to the Weighted Fuzzy Time Series

Vianita

2024

JTAM

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The improvement of plantation forecasting accuracy, particularly with regard to coffee production, was an essential aspect of earth observations for the purpose of informing plantation management alternatives. These decisions included strategic and tactical decisions on supply chain operations and financial decisions. Many research initiatives have used a variety of methodologies to the forecasting of plantation areas and related industries, such as coffee production. One of these methods was known as the fuzzy time series (FTS) technique. This study combined ratio-interval and frequency density to get universe of discourse and partition followed by adopted weighted and modified that weighted. The first step was defined universe of discourse using ratio-interval algorithm. The second step was partition the universe of discourse using ratio-interval algorithm followed by frequency density partitioning. The third step was fuzzyfication. The fourth step built fuzzy logic relationship (FLR) and fuzzy logic relationship group (FLRG). The fifth step was adopted the modification weighted. The last step was defuzzyfication. The models evaluated by average forecasting error rate (AFER) and compared with existing methods. AFER value 1.24% for proposed method.

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Fuzzy time series model based on red–black trees for stock index forecasting

Cited by 7 publications

References 25 publications

A systematic literature survey on recent trends in stock market prediction

A systematic literature survey on recent trends in stock market prediction

A Novel Multifactor Type-2 Fuzzy Time Series Model based on Improved Fuzzy C-means Algorithm and Justifiable Granularity for Stock Index Forecasting

Ratio Interval-Frequency Density with Modifications to the Weighted Fuzzy Time Series

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