IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v523y2019icp908-914.html
   My bibliography  Save this article

tvf-EMD based time series analysis of 7Be sampled at the CTBTO-IMS network

Author

Listed:
  • Longo, Alessandro
  • Bianchi, Stefano
  • Plastino, Wolfango

Abstract

A methodology of adaptive time series analysis based on Empirical Mode Decomposition (EMD) has been applied to investigate Be7 activity concentration variability, along with temperature. Analysed data were sampled daily at ground level by 28 different stations of the CTBTO-IMS network. The adopted methodology allows to characterise trend component, yearly cycles and outlier occurrence of Be7. Trend component is first estimated via simple EMD and removed. The recent time-varying filter EMD (tvf-EMD) technique is instead employed to extract local narrowband oscillatory modes from the detrended data. Denoising is carried out using a threshold on the Hurst exponent of extracted oscillatory modes (IMFs). It is found that the phase of the yearly cycles is shifted at high latitudes, possibly due to the Hadley cell dynamics. Furthermore, high values of Be7 yearly cycle are found for some stations in 2009–2010. Due to their location, this is possibly due to the El Niño event occurring that year. Though, further studies are needed in this regard.

Suggested Citation

  • Longo, Alessandro & Bianchi, Stefano & Plastino, Wolfango, 2019. "tvf-EMD based time series analysis of 7Be sampled at the CTBTO-IMS network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 908-914.
    • Handle: RePEc:eee:phsmap:v:523:y:2019:i:c:p:908-914
    DOI: 10.1016/j.physa.2019.04.111
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437119304881
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2019.04.111?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Dongxiao Niu & Yi Liang & Wei-Chiang Hong, 2017. "Wind Speed Forecasting Based on EMD and GRNN Optimized by FOA," Energies, MDPI, vol. 10(12), pages 1-18, December.
    2. Bianchi, Stefano & Longo, Alessandro & Plastino, Wolfango, 2018. "A new methodological approach for worldwide beryllium-7 time series analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 501(C), pages 377-387.
    3. Sarvan, D. & Stratimirović, Đ. & Blesić, S. & Djurdjevic, V. & Miljković, V. & Ajtić, J., 2017. "Dynamics of beryllium-7 specific activity in relation to meteorological variables, tropopause height, teleconnection indices and sunspot number," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 813-823.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Du, Pei & Guo, Ju’e & Sun, Shaolong & Wang, Shouyang & Wu, Jing, 2021. "Multi-step metal prices forecasting based on a data preprocessing method and an optimized extreme learning machine by marine predators algorithm," Resources Policy, Elsevier, vol. 74(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yi Liang & Haichao Wang, 2021. "Using Improved SPA and ICS-LSSVM for Sustainability Assessment of PV Industry along the Belt and Road," Energies, MDPI, vol. 14(12), pages 1-19, June.
    2. Sandra Minerva Valdivia-Bautista & José Antonio Domínguez-Navarro & Marco Pérez-Cisneros & Carlos Jesahel Vega-Gómez & Beatriz Castillo-Téllez, 2023. "Artificial Intelligence in Wind Speed Forecasting: A Review," Energies, MDPI, vol. 16(5), pages 1-28, March.
    3. Liang, Yi & Niu, Dongxiao & Hong, Wei-Chiang, 2019. "Short term load forecasting based on feature extraction and improved general regression neural network model," Energy, Elsevier, vol. 166(C), pages 653-663.
    4. Wenlong Fu & Kai Wang & Jianzhong Zhou & Yanhe Xu & Jiawen Tan & Tie Chen, 2019. "A Hybrid Approach for Multi-Step Wind Speed Forecasting Based on Multi-Scale Dominant Ingredient Chaotic Analysis, KELM and Synchronous Optimization Strategy," Sustainability, MDPI, vol. 11(6), pages 1-24, March.
    5. Bianchi, Stefano & Longo, Alessandro & Plastino, Wolfango, 2018. "A new methodological approach for worldwide beryllium-7 time series analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 501(C), pages 377-387.
    6. Liu, Hui & Chen, Chao, 2019. "Data processing strategies in wind energy forecasting models and applications: A comprehensive review," Applied Energy, Elsevier, vol. 249(C), pages 392-408.
    7. Lin, Shengmao & Wang, Shu & Xu, Xuefang & Li, Ruixiong & Shi, Peiming, 2024. "GAOformer: An adaptive spatiotemporal feature fusion transformer utilizing GAT and optimizable graph matrixes for offshore wind speed prediction," Energy, Elsevier, vol. 292(C).
    8. Peng Lu & Lin Ye & Bohao Sun & Cihang Zhang & Yongning Zhao & Jingzhu Teng, 2018. "A New Hybrid Prediction Method of Ultra-Short-Term Wind Power Forecasting Based on EEMD-PE and LSSVM Optimized by the GSA," Energies, MDPI, vol. 11(4), pages 1-23, March.
    9. Minfeng Wu & Wen Chen, 2022. "Forecast of Electric Vehicle Sales in the World and China Based on PCA-GRNN," Sustainability, MDPI, vol. 14(4), pages 1-14, February.
    10. Li, Jingrui & Wang, Jiyang & Li, Zhiwu, 2023. "A novel combined forecasting system based on advanced optimization algorithm - A study on optimal interval prediction of wind speed," Energy, Elsevier, vol. 264(C).
    11. Dong, Yingchao & Zhang, Hongli & Wang, Cong & Zhou, Xiaojun, 2021. "A novel hybrid model based on Bernstein polynomial with mixture of Gaussians for wind power forecasting," Applied Energy, Elsevier, vol. 286(C).
    12. Hongwei Wang & Yuansheng Huang & Chong Gao & Yuqing Jiang, 2019. "Cost Forecasting Model of Transformer Substation Projects Based on Data Inconsistency Rate and Modified Deep Convolutional Neural Network," Energies, MDPI, vol. 12(16), pages 1-21, August.
    13. Pin Li & Jin-Suo Zhang, 2018. "A New Hybrid Method for China’s Energy Supply Security Forecasting Based on ARIMA and XGBoost," Energies, MDPI, vol. 11(7), pages 1-28, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:523:y:2019:i:c:p:908-914. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.