Interval prediction approach to crude oil price based on three-way clustering and decomposition ensemble learning

Sun, Bingzhen; Bai, Juncheng; Chu, Xiaoli; Sun, Shaolong; Li, Yongwu; Li, Hongtao

doi:10.1016/j.asoc.2022.108933

Cited by 27 publications

(4 citation statements)

References 49 publications

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“…(Sun et al. 2022 ) talked over the three-way clustering algorithm drew on entropy and applied it to interval prediction.…”

Section: A Systematic Review Of 3wdmentioning

confidence: 99%

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Three-way decisions in generalized intuitionistic fuzzy environments: survey and challenges

Ding,

Zhang,

et al. 2024

Artif Intell Rev

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Enhancing decision-making under risks is crucial in various fields, and three-way decision (3WD) methods have been extensively utilized and proven to be effective in numerous scenarios. However, traditional methods may not be sufficient when addressing intricate decision-making scenarios characterized by uncertain and ambiguous information. In response to this challenge, the generalized intuitionistic fuzzy set (IFS) theory extends the conventional fuzzy set theory by introducing two pivotal concepts, i.e., membership degrees and non-membership degrees. These concepts offer a more comprehensive means of portraying the relationship between elements and fuzzy concepts, thereby boosting the ability to model complex problems. The generalized IFS theory brings about heightened flexibility and precision in problem-solving, allowing for a more thorough and accurate description of intricate phenomena. Consequently, the generalized IFS theory emerges as a more refined tool for articulating fuzzy phenomena. The paper offers a thorough review of the research advancements made in 3WD methods within the context of generalized intuitionistic fuzzy (IF) environments. First, the paper summarizes fundamental aspects of 3WD methods and the IFS theory. Second, the paper discusses the latest development trends, including the application of these methods in new fields and the development of new hybrid methods. Furthermore, the paper analyzes the strengths and weaknesses of research methods employed in recent years. While these methods have yielded impressive outcomes in decision-making, there are still some limitations and challenges that need to be addressed. Finally, the paper proposes key challenges and future research directions. Overall, the paper offers a comprehensive and insightful review of the latest research progress on 3WD methods in generalized IF environments, which can provide guidance for scholars and engineers in the intelligent decision-making field with situations characterized by various uncertainties.

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“…(Sun et al. 2022 ) talked over the three-way clustering algorithm drew on entropy and applied it to interval prediction.…”

Section: A Systematic Review Of 3wdmentioning

confidence: 99%

“… Three-way clustering (Sun et al. 2022 ) relies on 3WD, requiring appropriate thresholds to partition experimental data into core, edge, and external regions. However, most thresholding methods for the three clustering algorithms are subjectively determined and one-sided.…”

Section: Challenges and Future Research Directionsmentioning

confidence: 99%

Three-way decisions in generalized intuitionistic fuzzy environments: survey and challenges

Ding,

Zhang,

et al. 2024

Artif Intell Rev

View full text Add to dashboard Cite

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“…As a result, it can significantly mitigate the decisionmaking risk associated with the aggregation of information granularity. For instance, Sun et al [21] introduced a novel approach for crude oil price prediction using the TWD clustering-based interval prediction system. However, it should be noted that the subjective determination of the loss function in this model based on experience does not ensure the reliability and accuracy of the decision outcomes.…”

Section: Introductionmentioning

confidence: 99%

A granularity time series forecasting model combining three-way decision and trend information granularity

Qiu,

Su,

Qian

2024

Preprint

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In recent decades, significant advancements have been made in the field of time series data mining, leading to its widespread application in various domains. However, the existence of data correlation in time series data sets brings challenges to long-term prediction. One approach to address this issue is to transform the original time series into granular time series (GTS). Therefore, a prediction model based on GTS is proposed to meet this challenge in this study. Firstly, the improved L1-trend filtering is used to achieve the optimal segmentation of information particles. Then, a three-way decisions (TWD) model based on shape similarity is used to compress and aggregate information granules. Finally, a GTS prediction model based on LSTM neural network is established. The model effectively retains the trend information of the time series and overcomes the limitation that the existing models cannot adjust the granularity length of the original information. In addition, the proposed model is applied to several real datasets for sensitivity analysis and comparative analysis. The results show that the model has strong performance in long-term forecasting.

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“…Exchange rate fluctuations have an important impact on national income, interest rates, etc. Therefore, the accurate analysis and forecast of exchange rate are essential 1 . The analysis and research on exchange rate forecast have attracted the attention of many scholars, and there have been a lot of research work.…”

Section: Introductionmentioning

confidence: 99%

RMB exchange rate forecasting algorithm and empirical analysis

Gu,

Du,

Guo

et al. 2023

Atlantis Highlights in Engineering

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This paper studies the forecasting algorithm of RMB exchange rate. Firstly, the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise algorithm (CEEMDAN) is used to decompose the exchange rate data into several intrinsic mode functions (IMF). Then the correlation coefficients of each IMF are calculated, and the high-frequency components with low correlation are regarded as noisy signals for filtering. The low frequency IMF components are forecast by Autoregressive Integrated Moving Average Model (ARIMA) time series model, while the high frequency IMF components are predicted by BP neural network model and Long Short Term Memory (LSTM) neural network model. Finally, the predicted values of each component are added to construct a new forecasting algorithm for the RMB exchange rate. In the empirical analysis, we collected the exchange rate data of RMB against US dollar and RMB against EUR every working day(FOREX opening hours Beijing time. Monday to Friday) from January 2, 2019 to December 22, 2022, and carry out empirical analysis of the exchange rate from December 23, 2022 to January 20, 2023 by using the prediction algorithm constructed in this paper. By comparing with the true value (the true exchange rate value published by FOREX), the average forecast error is 0.1779 % (RMB/USD) and 0.2072 % (RMB/EUR) respectively. It should be emphasized that our forecasting method can more precisely predict the RMB exchange rate over four weeks, which provides sufficient buffer time for the foreign exchange management department to conduct supervision and formulate countermeasures.

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Interval prediction approach to crude oil price based on three-way clustering and decomposition ensemble learning

Cited by 27 publications

References 49 publications

Three-way decisions in generalized intuitionistic fuzzy environments: survey and challenges

Three-way decisions in generalized intuitionistic fuzzy environments: survey and challenges

A granularity time series forecasting model combining three-way decision and trend information granularity

RMB exchange rate forecasting algorithm and empirical analysis

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