Point and interval prediction of crude oil futures prices based on chaos theory and multiobjective slime mold algorithm

Sun, Weixin; Chen, Heli; Liu, Feng; Wang, Yong

doi:10.1007/s10479-022-04781-6

Cited by 10 publications

(4 citation statements)

References 49 publications

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“…It includes chaotic time series, external neural networks, linear model prediction, and deep learning. In this step, the values that the sub-models have predicted are mixed with the ideal weight established by MOSMA [ 102 ]. By applying MOSMA to identify CIACs, the outcomes of IF were improved to have a smaller width and greater prediction accuracy.…”

Section: Methods Of Smamentioning

confidence: 99%

Slime Mould Algorithm: A Comprehensive Survey of Its Variants and Applications

Gharehchopogh

Uçan

İbrikçi

et al. 2023

Arch Computat Methods Eng

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Meta-heuristic algorithms have a high position among academic researchers in various fields, such as science and engineering, in solving optimization problems. These algorithms can provide the most optimal solutions for optimization problems. This paper investigates a new meta-heuristic algorithm called Slime Mould algorithm (SMA) from different optimization aspects. The SMA algorithm was invented due to the fluctuating behavior of slime mold in nature. It has several new features with a unique mathematical model that uses adaptive weights to simulate the biological wave. It provides an optimal pathway for connecting food with high exploration and exploitation ability. As of 2020, many types of research based on SMA have been published in various scientific databases, including IEEE, Elsevier, Springer, Wiley, Tandfonline, MDPI, etc. In this paper, based on SMA, four areas of hybridization, progress, changes, and optimization are covered. The rate of using SMA in the mentioned areas is 15, 36, 7, and 42%, respectively. According to the findings, it can be claimed that SMA has been repeatedly used in solving optimization problems. As a result, it is anticipated that this paper will be beneficial for engineers, professionals, and academic scientists.

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Section: Methods Of Smamentioning

confidence: 99%

Slime Mould Algorithm: A Comprehensive Survey of Its Variants and Applications

Gharehchopogh

Uçan

İbrikçi

et al. 2023

Arch Computat Methods Eng

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“…The second layer is the hidden layer, and the number of nodes depends on the specific problem. The third layer is the output layer y q (q = 1, 2, ⋯ , m) (Sun et al 2022a). The network structure is shown in Fig.…”

Section: Methodsmentioning

confidence: 99%

Water resource carrying capacity and obstacle factors in the Yellow River basin based on the RBF neural network model

Sun

Zhou

Wang

2022

Environ Sci Pollut Res

Self Cite

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The Yellow River basin (YRB) plays an important role in China's economic and social growth. Based on different dimensions, we adopted the radial basis function (RBF) neural network model and the obstacle degree model to examine the water resource carrying capacity (WRCC) of the YRB. From 2005 to 2020, the WRCC of the entire YRB, as well as the upstream and midstream regions, improved, but the WRCC of the downstream region remained poor, revealing spatial differences. The overall improvement in the WRCC of the Yellow River's nine provinces is good, but the WRCC of Inner Mongolia and Henan is poor, suggesting regional differences. From the standpoint of obstacle factors, the development and usage rate of surface water resources are the main challenges. In 2020, the obstacle degree of the YRB reached 87.4871%. The irrigated area rate in Gansu was the primary obstacle factor, and the obstacle degree reached 73.0238%. Qinghai's industrial aspects mostly hindered the improvement of its WRCC, with an obstacle degree of 31.36%. The results provide a theoretical reference for the high-quality development of the YRB.

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“…Due to the strong self-learning and self-organizing abilities of artificial intelligence methods, intelligent models related to machine learning are becoming increasingly popular in wind energy prediction [17]. Artificial intelligence models regard wind speed forecast as a stochastic procedure and uses past observation data to mine the timevary relevance in the wind speed sequence for prediction [18]. Common AI models contain artificial neural networks (Anns), support vector machines (SVM), and fuzzy logic methods (FL), which are widely used for wind speed prediction [19]- [20].…”

Section: Artificial Intelligence Modelmentioning

confidence: 99%

Hybrid Neural Network Wind Speed Prediction Based on Secondary Decomposition and Weighted Averaging

Bi,

Bai,

Hou

et al. 2024

Preprint

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The randomicity and fluctuation of the wind speed will influence the precision of the forecast. To improve the precision of wind speed forecast, this paper presents a new method of combined wind speed forecast based on the second decomposition and weighted average. First, the ICEEMDAN decomposition method is used to get different sub-sequences, and then the fuzzy entropy is used to judge the degree of confusion of the sub-sequences. In this paper, the ARIMA model is used to predict the minimum fuzzy entropy. And the other subsequences are decomposed by BPNN, VMD and predicted by NAR and BP neural network with suitable weighting ratio for weighted average and PSO-LSTM neural network respectively, and ultimately all the predicted values are superimposed to get the final prediction. Experiments were conducted using three datasets and eight comparison models to verify the validity of this model. The prediction analysis was carried out using the actual measured data of a wind farm in Inner Mongolia, and the results indicated that (1) using fuzzy entropy can effectively improve the prediction precision; (2) the prediction accuracy of the combined prediction method of neural network based on secondary decomposition was greatly improved and the prediction results were more reliable; (3) Decompose one of the subsequences with VMD, predict it with NAR and BP neural network, and choose appropriate weight ratio for weighted average prediction will achieve better prediction results; (4) the root mean square error (RMSE) of the hybrid model on the three wind speed 1 datasets were 0.28777, 0.22786 and 0.17128, which are lower than the comparison values of other models. So, it is workable to use this hybrid model in wind speed prediction.

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Point and interval prediction of crude oil futures prices based on chaos theory and multiobjective slime mold algorithm

Cited by 10 publications

References 49 publications

Slime Mould Algorithm: A Comprehensive Survey of Its Variants and Applications

Slime Mould Algorithm: A Comprehensive Survey of Its Variants and Applications

Water resource carrying capacity and obstacle factors in the Yellow River basin based on the RBF neural network model

Hybrid Neural Network Wind Speed Prediction Based on Secondary Decomposition and Weighted Averaging

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