Green Bond Index Prediction Based on CEEMDAN-LSTM

Wang, Jiaqi; Tang, Jiulin; Guo, Kun

doi:10.3389/fenrg.2021.793413

Cited by 9 publications

(9 citation statements)

References 93 publications

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“…Researchers want to comprehend the procedures, regulations, and financial tools that can successfully encourage and support energy efficiency projects, which will ultimately contribute to a greener and more sustainable future. This is why they are focused on energy efficiency within the context of green finance [2,14,60,67,69,74,106,117,134,136,156,160,170].…”

Section: Discussionmentioning

confidence: 99%

Emerging new themes in green finance: a systematic literature review

Mudalige

2023

Futur Bus J

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There is a need for an extensive understanding of the emerging themes and trends within the domain of green finance, which is still evolving. By conducting a systematic literature review on green finance, the purpose of this study is to identify the emerging themes that have garnered significant attention over the past 12 years. In order to identify the emerging themes in green finance, bibliometric analysis was performed on 978 publications that were published between 2011 and 2023 and were taken from the databases of Scopus and Web of Science. The author examined annual scientific production, journal distribution, countries scientific production, most relevant authors, most frequent words, areas where empirical research is lacking, words' frequency over time, trend topics, and themes of green finance. The outcome of the review identified the following seven themes: (i) green finance and environmental sustainability; (ii) green finance and investments; (iii) green finance and innovation; (iv) green finance policy/green credit guidelines; (v) green finance and economy; (vi) green finance and corporate social responsibility; (vii)trends/challenges/barriers/awareness of green finance. The analysis of these emerging themes will contribute to the existing corpus of knowledge and provide valuable insights into the landscape of green finance as it evolves.

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Section: Discussionmentioning

confidence: 99%

Emerging new themes in green finance: a systematic literature review

Mudalige

2023

Futur Bus J

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“…a variation of an LSTM model known as a Complete Ensemble Empirical Mode Decomposition with Adaptive Noise-LSTM (CEEMDAN-LSTM) model and Neural Basis Expansion Analysis for Interpretable Time Series (N-BEATS) model. The CEEMDAN-LSTM model is explored by Wang et al (2022) on green bonds (see 4 below). The N-BEATS model is a time series model and is explored by Oreshkin et al (2019).…”

Section: Executive Summarymentioning

confidence: 99%

“…move to a multivariate analysis in subsequent papers. This is examined in Wang et al (2022) which, when coupled with the CEEMDAN-LSTM model, seems to produce materially improved model predictions based on green bond time series data.…”

Section: Executive Summarymentioning

confidence: 99%

“…For example, Cui et al (2022) examines how green bonds have reacted to the COVID pandemic. Expand the neural network models used to more exotic models such as a CEEMDAN-LSTM architecture or N-BEATS architecture. For example, the research paper by Wang et al (2022) suggests that a CEEMDAN-LSTM is more effective for time series analysis on green bonds compared to an LSTM model when analysing a time series on green bonds. The N-BEATS model is explored in Oreshkin et al (2019) and has been designed with the specific aim of tackling time series problems.…”

Section: Conclusion and Next Stepsmentioning

confidence: 99%

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Time series analysis of GSS bonds

Dey

2024

Br. Actuar. J.

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The market for green bonds, and environmentally aligned investment solutions, is increasing. As of 2022, the market of green bonds exceeded USD 2 trillion in issuance, with India, for example, having issued its first-ever sovereign green bonds totally R80bn (c.USD1bn) in January 2023. This paper lays the foundation for future papers and summarises the initial stages of our analysis, where we try to replicate the S&P Green Bond Index (i.e. this is a time series problem) over a period using non-traditional techniques. The models we use include neural networks such as CNNs, LSTMs and GRUs. We extend our analysis and use an open-source decision tree model called XGBoost. For the purposes of this paper, we use 1 day’s prior index information to predict today’s value and repeat this over a period of time. We ignore for example stationarity considerations and extending the input window/output horizon in our analysis, as these will be discussed in future papers. The paper explains the methodology used in our analysis, gives details of general underlying background information to the architecture models (CNNs, LSTMs, GRUs and XGBoost), as well as background to regularisation techniques specifically L2 regularisation, loss curves and hyperparameter optimisation, in particular, the open-source library Optuna.

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“…The procedure for mapping the third-party continuous glucose monitoring data to new data from a particular user using only a few invasive measurements from that user were as follows [15]:…”

Section: Mapping Of the Datamentioning

confidence: 99%

Blood glucose estimation using third person continuous glucose monitoring data via complete ensemble empirical mode decomposition with adaptive noise and long-short-term memory networks

Wing-Kuen

Liang

Zhao

et al. 2023

Preprint

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Background Blood glucose estimation is critical for monitoring the health of patients with diabetes. Traditionally, it has been performed invasively, via pricking a fingertip to draw a small amount of blood for analysis. However, this type of approach causes pain to patients in the long-term. More recently, micro-invasive approaches have been proposed as alternatives; however, these new methods can be quite costly. Methods To address this issue, we proposed a minimally invasive approach for obtaining blood glucose measurements. In our proposed approach, the continuous glucose monitoring data of a third person are mapped to more sparse data from a particular user, acquired through traditional invasive means. The complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method is then applied to the mapped glucose data. The first six intrinsic mode functions are discarded, and a long short-term memory (LSTM) network is used to perform non-invasive blood glucose estimation. To demonstrate the effectiveness of our proposed method, a loss function was used as a performance metric. Our approach was compared to the LSTM network, both with and without an empirical mode decomposition (EMD) approach. Results Numerical simulation results showed that our approach achieved a higher accuracy than either of these two popular methods. Conclusions Our method can be used to improve the accuracy of blood glucose prediction.

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Green Bond Index Prediction Based on CEEMDAN-LSTM

Cited by 9 publications

References 93 publications

Emerging new themes in green finance: a systematic literature review

Emerging new themes in green finance: a systematic literature review

Time series analysis of GSS bonds

Blood glucose estimation using third person continuous glucose monitoring data via complete ensemble empirical mode decomposition with adaptive noise and long-short-term memory networks

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