Do the global grain spot markets exhibit multifractal nature?

Gao, Xing-Lu; Shao, Ying-Hui; Yang, Yanhong; Zhou, Wei‐Xing

doi:10.1016/j.chaos.2022.112663

Cited by 32 publications

(6 citation statements)

References 78 publications

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“…On the contrary, the Hurst exponent of the GOI indices of maize and soyabeans are 0.5405 and 0.5200, which are very close to 0.5, implying that the GOI indices of maize and soyabeans are close to Brownian motion. These results agree with the Hurst exponents obtained by using the detrending moving average analysis [43].…”

Section: Hurst Exponent Of Six Goi Indicessupporting

confidence: 90%

Geography of Technology Transfer in China

Liu

2023

East China Normal University Scientific Reports

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Graphs can model complex relationships between objects, enabling a myriad of Web applications such as online page/article classification and social recommendation. While graph neural networks (GNNs) have emerged as a powerful tool for graph representation learning, in an end-to-end supervised setting, their performance heavily relies on a large amount of task-specific supervision. To reduce labeling requirement, the "pre-train, fine-tune" and "pre-train, prompt" paradigms have become increasingly common. In particular, prompting is a popular alternative to fine-tuning in natural language processing, which is designed to narrow the gap between pre-training and downstream objectives in a task-specific manner. However, existing study of prompting on graphs is still limited, lacking a universal treatment to appeal to different downstream tasks. In this paper, we propose GraphPrompt, a novel pre-training and prompting framework on graphs. GraphPrompt not only unifies pre-training and downstream tasks into a common task template, but also employs a learnable prompt to assist a downstream task in locating the most relevant knowledge from the pre-trained model in a task-specific manner. Finally, we conduct extensive experiments on five public datasets to evaluate and analyze GraphPrompt. CCS CONCEPTS• Computing methodologies → Learning latent representations; • Information systems → Data mining.

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Section: Hurst Exponent Of Six Goi Indicessupporting

confidence: 90%

Geography of Technology Transfer in China

Liu

2023

East China Normal University Scientific Reports

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“…Interestingly, Gao et al. [50] demonstrated that not all the grain indices owned multifractal behaviors.…”

Section: Computational Resultsmentioning

confidence: 99%

Role of vaccine in fighting the variants of COVID-19

Wang

Jiang

et al. 2023

Chaos, Solitons & Fractals

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“…4 depicts the return time series. In Quantitative Finance and Econophysics, it is usual to use the MF-DCCA considering the return series [29,30,31,32,33]. However, performing a first-order differentiation process excludes the trend of the original series.…”

Section: Resultsmentioning

confidence: 99%

Multifractal Detrended Cross-Correlation Analysis: The case of Brazilian Bioethanol time series

Fernandes¹,

Silva²,

Araujo³

et al. 2023

Preprint

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We have examined the nonlinear cross-correlation between the São Paulo time series of the weekly price of Bioethanol and the other 14 Brazilian capitals’ time series of the weekly price of the same biofuel using the Multifractal Detrended Cross-Correlation Analysis (MFDCCA). We provide evidence of multifractal correlation properties across these prices. We implement our method using both prices and returns and show some divergence between results. We recommend using price time series when using MF-DCCA.

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Do the global grain spot markets exhibit multifractal nature?

Cited by 32 publications

References 78 publications

Geography of Technology Transfer in China

Geography of Technology Transfer in China

Role of vaccine in fighting the variants of COVID-19

Multifractal Detrended Cross-Correlation Analysis: The case of Brazilian Bioethanol time series

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