IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v9y2021i10p1122-d555380.html
   My bibliography  Save this article

Hybrid Model for Time Series of Complex Structure with ARIMA Components

Author

Listed:
  • Oksana Mandrikova

    (Institute of Cosmophysical Research and Radio Wave Propagation, Far Eastern Branch of the Russian Academy of Sciences, Mirnaya st, 7, Paratunka, 684034 Kamchatskiy Kray, Russia)

  • Nadezhda Fetisova

    (Institute of Cosmophysical Research and Radio Wave Propagation, Far Eastern Branch of the Russian Academy of Sciences, Mirnaya st, 7, Paratunka, 684034 Kamchatskiy Kray, Russia)

  • Yuriy Polozov

    (Institute of Cosmophysical Research and Radio Wave Propagation, Far Eastern Branch of the Russian Academy of Sciences, Mirnaya st, 7, Paratunka, 684034 Kamchatskiy Kray, Russia)

Abstract

A hybrid model for the time series of complex structure (HMTS) was proposed. It is based on the combination of function expansions in a wavelet series with ARIMA models. HMTS has regular and anomalous components. The time series components, obtained after expansion, have a simpler structure that makes it possible to identify the ARIMA model if the components are stationary. This allows us to obtain a more accurate ARIMA model for a time series of complicated structure and to extend the area for application. To identify the HMTS anomalous component, threshold functions are applied. This paper describes a technique to identify HMTS and proposes operations to detect anomalies. With the example of an ionospheric parameter time series, we show the HMTS efficiency, describe the results and their application in detecting ionospheric anomalies. The HMTS was compared with the nonlinear autoregression neural network NARX, which confirmed HMTS efficiency.

Suggested Citation

  • Oksana Mandrikova & Nadezhda Fetisova & Yuriy Polozov, 2021. "Hybrid Model for Time Series of Complex Structure with ARIMA Components," Mathematics, MDPI, vol. 9(10), pages 1-18, May.
  • Handle: RePEc:gam:jmathe:v:9:y:2021:i:10:p:1122-:d:555380
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/9/10/1122/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/9/10/1122/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Junyan Liu & Sandeep Kumar & Daniel P. Palomar, 2018. "Parameter Estimation of Heavy-Tailed AR Model with Missing Data via Stochastic EM," Papers 1809.07203, arXiv.org, revised Feb 2019.
    2. Julio Cezar Souza Vasconcelos & Gauss Moutinho Cordeiro & Edwin Moises Marcos Ortega & Édila Maria de Rezende, 2021. "A new regression model for bimodal data and applications in agriculture," Journal of Applied Statistics, Taylor & Francis Journals, vol. 48(2), pages 349-372, January.
    3. Zina Boussaada & Octavian Curea & Ahmed Remaci & Haritza Camblong & Najiba Mrabet Bellaaj, 2018. "A Nonlinear Autoregressive Exogenous (NARX) Neural Network Model for the Prediction of the Daily Direct Solar Radiation," Energies, MDPI, vol. 11(3), pages 1-21, March.
    4. Dragan Miljkovic & Saleem Shaik & Silvia Miranda & Nikita Barabanov & Anais Liogier, 2015. "Globalisation and Obesity," The World Economy, Wiley Blackwell, vol. 38(8), pages 1278-1294, August.
    Full references (including those not matched with items on IDEAS)

    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. Oksana Mandrikova & Yuryi Polozov & Nataly Zhukova & Yulia Shichkina, 2022. "Approximation and Analysis of Natural Data Based on NARX Neural Networks Involving Wavelet Filtering," Mathematics, MDPI, vol. 10(22), pages 1-16, November.
    2. Giuntella, Osea & Rieger, Matthias & Rotunno, Lorenzo, 2020. "Weight gains from trade in foods: Evidence from Mexico," Journal of International Economics, Elsevier, vol. 122(C).
    3. Timothy Praditia & Thilo Walser & Sergey Oladyshkin & Wolfgang Nowak, 2020. "Improving Thermochemical Energy Storage Dynamics Forecast with Physics-Inspired Neural Network Architecture," Energies, MDPI, vol. 13(15), pages 1-26, July.
    4. Anshuman Satapathy & Niranjan Nayak & Tanmoy Parida, 2022. "Real-Time Power Quality Enhancement in a Hybrid Micro-Grid Using Nonlinear Autoregressive Neural Network," Energies, MDPI, vol. 15(23), pages 1-35, November.
    5. Guilherme Henrique Alves & Geraldo Caixeta Guimarães & Fabricio Augusto Matheus Moura, 2023. "Battery Storage Systems Control Strategies with Intelligent Algorithms in Microgrids with Dynamic Pricing," Energies, MDPI, vol. 16(14), pages 1-30, July.
    6. Fjelkestam Frederiksen, Cornelia A. & Cai, Zuansi, 2022. "Novel machine learning approach for solar photovoltaic energy output forecast using extra-terrestrial solar irradiance," Applied Energy, Elsevier, vol. 306(PB).
    7. Sufyan Samara & Emad Natsheh, 2020. "Intelligent PV Panels Fault Diagnosis Method Based on NARX Network and Linguistic Fuzzy Rule-Based Systems," Sustainability, MDPI, vol. 12(5), pages 1-20, March.
    8. Salma Hamad Almuhaini & Nahid Sultana, 2023. "Forecasting Long-Term Electricity Consumption in Saudi Arabia Based on Statistical and Machine Learning Algorithms to Enhance Electric Power Supply Management," Energies, MDPI, vol. 16(4), pages 1-28, February.
    9. Jee-Heon Kim & Nam-Chul Seong & Wonchang Choi, 2020. "Forecasting the Energy Consumption of an Actual Air Handling Unit and Absorption Chiller Using ANN Models," Energies, MDPI, vol. 13(17), pages 1-12, August.
    10. Lisa Oberlander & Anne‐Célia Disdier & Fabrice Etilé, 2017. "Globalisation and national trends in nutrition and health: A grouped fixed‐effects approach to intercountry heterogeneity," Health Economics, John Wiley & Sons, Ltd., vol. 26(9), pages 1146-1161, September.
    11. Jee-Heon Kim & Nam-Chul Seong & Wonchang Choi, 2019. "Cooling Load Forecasting via Predictive Optimization of a Nonlinear Autoregressive Exogenous (NARX) Neural Network Model," Sustainability, MDPI, vol. 11(23), pages 1-13, November.
    12. Mary, Sebastien & Shaw, Kelsey & Colen, Liesbeth & Gomez y Paloma, Sergio, 2020. "Does agricultural aid reduce child stunting?," World Development, Elsevier, vol. 130(C).
    13. Karodine Chreng & Han Soo Lee & Soklin Tuy, 2022. "A Hybrid Model for Electricity Demand Forecast Using Improved Ensemble Empirical Mode Decomposition and Recurrent Neural Networks with ERA5 Climate Variables," Energies, MDPI, vol. 15(19), pages 1-26, October.
    14. Hernandez-Matheus, Alejandro & Löschenbrand, Markus & Berg, Kjersti & Fuchs, Ida & Aragüés-Peñalba, Mònica & Bullich-Massagué, Eduard & Sumper, Andreas, 2022. "A systematic review of machine learning techniques related to local energy communities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 170(C).
    15. Jee-Heon Kim & Nam-Chul Seong & Wonchang Choi, 2019. "Modeling and Optimizing a Chiller System Using a Machine Learning Algorithm," Energies, MDPI, vol. 12(15), pages 1-13, July.
    16. Sylwia Radomska, 2021. "Prognozowanie indeksu WIG20 za pomocą sieci neuronowych NARX i metody SVM," Bank i Kredyt, Narodowy Bank Polski, vol. 52(5), pages 457-472.
    17. Barrera, Emiliano Lopez & Miljkovic, Dragan, 2022. "The link between the two epidemics provides an opportunity to remedy obesity while dealing with Covid-19," Journal of Policy Modeling, Elsevier, vol. 44(2), pages 280-297.
    18. Lee, Juyong & Cho, Youngsang, 2022. "National-scale electricity peak load forecasting: Traditional, machine learning, or hybrid model?," Energy, Elsevier, vol. 239(PD).
    19. Sébastien Mary & Avraham Stoler, 2021. "Does agricultural trade liberalization increase obesity in developing countries?," Review of Development Economics, Wiley Blackwell, vol. 25(3), pages 1326-1350, August.
    20. SeyedAli Ghahari & Cesar Queiroz & Samuel Labi & Sue McNeil, 2021. "Cluster Forecasting of Corruption Using Nonlinear Autoregressive Models with Exogenous Variables (NARX)—An Artificial Neural Network Analysis," Sustainability, MDPI, vol. 13(20), pages 1-20, October.

    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:gam:jmathe:v:9:y:2021:i:10:p:1122-:d:555380. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.