IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i10p2578-d360302.html
   My bibliography  Save this article

ForecastTB—An R Package as a Test-Bench for Time Series Forecasting—Application of Wind Speed and Solar Radiation Modeling

Author

Listed:
  • Neeraj Dhanraj Bokde

    (Department of Engineering—Renewable Energy and Thermodynamics, Aarhus University, 8000 Aarhus, Denmark)

  • Zaher Mundher Yaseen

    (Sustainable Developments in Civil Engineering Research Group, Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam)

  • Gorm Bruun Andersen

    (Department of Engineering—Renewable Energy and Thermodynamics, Aarhus University, 8000 Aarhus, Denmark)

Abstract

This paper introduces an R package ForecastTB that can be used to compare the accuracy of different forecasting methods as related to the characteristics of a time series dataset. The ForecastTB is a plug-and-play structured module, and several forecasting methods can be included with simple instructions. The proposed test-bench is not limited to the default forecasting and error metric functions, and users are able to append, remove, or choose the desired methods as per requirements. Besides, several plotting functions and statistical performance metrics are provided to visualize the comparative performance and accuracy of different forecasting methods. Furthermore, this paper presents real application examples with natural time series datasets (i.e., wind speed and solar radiation) to exhibit the features of the ForecastTB package to evaluate forecasting comparison analysis as affected by the characteristics of a dataset. Modeling results indicated the applicability and robustness of the proposed R package ForecastTB for time series forecasting.

Suggested Citation

  • Neeraj Dhanraj Bokde & Zaher Mundher Yaseen & Gorm Bruun Andersen, 2020. "ForecastTB—An R Package as a Test-Bench for Time Series Forecasting—Application of Wind Speed and Solar Radiation Modeling," Energies, MDPI, vol. 13(10), pages 1-24, May.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:10:p:2578-:d:360302
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/10/2578/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/10/2578/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hyndman, Rob J. & Khandakar, Yeasmin, 2008. "Automatic Time Series Forecasting: The forecast Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i03).
    2. Federico Divina & Miguel García Torres & Francisco A. Goméz Vela & José Luis Vázquez Noguera, 2019. "A Comparative Study of Time Series Forecasting Methods for Short Term Electric Energy Consumption Prediction in Smart Buildings," Energies, MDPI, vol. 12(10), pages 1-23, May.
    3. Adamuthe, Amol C. & Thampi, Gopakumaran T., 2019. "Technology forecasting: A case study of computational technologies," Technological Forecasting and Social Change, Elsevier, vol. 143(C), pages 181-189.
    4. Wickham, Hadley, 2007. "Reshaping Data with the reshape Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 21(i12).
    5. Kisi, Ozgur & Heddam, Salim & Yaseen, Zaher Mundher, 2019. "The implementation of univariable scheme-based air temperature for solar radiation prediction: New development of dynamic evolving neural-fuzzy inference system model," Applied Energy, Elsevier, vol. 241(C), pages 184-195.
    6. Neeraj Bokde & Andrés Feijóo & Daniel Villanueva & Kishore Kulat, 2019. "A Review on Hybrid Empirical Mode Decomposition Models for Wind Speed and Wind Power Prediction," Energies, MDPI, vol. 12(2), pages 1-42, January.
    7. Aditya Gupta & Neeraj Bokde & K. D. Kulat, 2018. "Hybrid Leakage Management for Water Network Using PSF Algorithm and Soft Computing Techniques," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(3), pages 1133-1151, February.
    8. Hai Tao & Sadeq Oleiwi Sulaiman & Zaher Mundher Yaseen & H. Asadi & Sarita Gajbhiye Meshram & M. A. Ghorbani, 2018. "What Is the Potential of Integrating Phase Space Reconstruction with SVM-FFA Data-Intelligence Model? Application of Rainfall Forecasting over Regional Scale," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(12), pages 3935-3959, September.
    9. Paola Arce & Jonathan Antognini & Werner Kristjanpoller & Luis Salinas, 2019. "Fast and Adaptive Cointegration Based Model for Forecasting High Frequency Financial Time Series," Computational Economics, Springer;Society for Computational Economics, vol. 54(1), pages 99-112, June.
    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. Ali O. Al-Sulttani & Amimul Ahsan & Basim A. R. Al-Bakri & Mahir Mahmod Hason & Nik Norsyahariati Nik Daud & S. Idrus & Omer A. Alawi & Elżbieta Macioszek & Zaher Mundher Yaseen, 2022. "Double-Slope Solar Still Productivity Based on the Number of Rubber Scraper Motions," Energies, MDPI, vol. 15(21), pages 1-34, October.
    2. Bokde, Neeraj Dhanraj & Tranberg, Bo & Andresen, Gorm Bruun, 2021. "Short-term CO2 emissions forecasting based on decomposition approaches and its impact on electricity market scheduling," Applied Energy, Elsevier, vol. 281(C).
    3. Isabella Yunfei Zeng & Shiqi Tan & Jianliang Xiong & Xuesong Ding & Yawen Li & Tian Wu, 2021. "Estimation of Real-World Fuel Consumption Rate of Light-Duty Vehicles Based on the Records Reported by Vehicle Owners," Energies, MDPI, vol. 14(23), pages 1-19, November.

    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. Bokde, Neeraj Dhanraj & Tranberg, Bo & Andresen, Gorm Bruun, 2021. "Short-term CO2 emissions forecasting based on decomposition approaches and its impact on electricity market scheduling," Applied Energy, Elsevier, vol. 281(C).
    2. Neeraj Bokde & Andrés Feijóo & Nadhir Al-Ansari & Siyu Tao & Zaher Mundher Yaseen, 2020. "The Hybridization of Ensemble Empirical Mode Decomposition with Forecasting Models: Application of Short-Term Wind Speed and Power Modeling," Energies, MDPI, vol. 13(7), pages 1-23, April.
    3. Kasım Zor & Özgür Çelik & Oğuzhan Timur & Ahmet Teke, 2020. "Short-Term Building Electrical Energy Consumption Forecasting by Employing Gene Expression Programming and GMDH Networks," Energies, MDPI, vol. 13(5), pages 1-24, March.
    4. Behm, Svenia & Haupt, Harry, 2020. "Predictability of hourly nitrogen dioxide concentration," Ecological Modelling, Elsevier, vol. 428(C).
    5. Gkillas, Konstantinos & Gupta, Rangan & Pierdzioch, Christian, 2020. "Forecasting realized oil-price volatility: The role of financial stress and asymmetric loss," Journal of International Money and Finance, Elsevier, vol. 104(C).
    6. Nahapetyan Yervand, 2019. "The benefits of the Velvet Revolution in Armenia: Estimation of the short-term economic gains using deep neural networks," Central European Economic Journal, Sciendo, vol. 53(6), pages 286-303, January.
    7. Augustinus, Benno A. & Blum, Moshe & Citterio, Sandra & Gentili, Rodolfo & Helman, David & Nestel, David & Schaffner, Urs & Müller-Schärer, Heinz & Lensky, Itamar M., 2022. "Ground-truthing predictions of a demographic model driven by land surface temperatures with a weed biocontrol cage experiment," Ecological Modelling, Elsevier, vol. 466(C).
    8. Dombi, József & Jónás, Tamás & Tóth, Zsuzsanna Eszter, 2018. "Modeling and long-term forecasting demand in spare parts logistics businesses," International Journal of Production Economics, Elsevier, vol. 201(C), pages 1-17.
    9. Amal A. Al-Shargabi & Abdulbasit Almhafdy & Dina M. Ibrahim & Manal Alghieth & Francisco Chiclana, 2021. "Tuning Deep Neural Networks for Predicting Energy Consumption in Arid Climate Based on Buildings Characteristics," Sustainability, MDPI, vol. 13(22), pages 1-20, November.
    10. Amita Gajewar & Gagan Bansal, 2016. "Revenue Forecasting for Enterprise Products," Papers 1701.06624, arXiv.org.
    11. Tao XIONG & Chongguang LI & Yukun BAO, 2017. "An improved EEMD-based hybrid approach for the short-term forecasting of hog price in China," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 63(3), pages 136-148.
    12. Pieter van der Spek & Chris Verhoef, 2014. "Balancing Time‐to‐Market and Quality in Embedded Systems," Systems Engineering, John Wiley & Sons, vol. 17(2), pages 166-192, June.
    13. Hyndman, Rob J. & Ahmed, Roman A. & Athanasopoulos, George & Shang, Han Lin, 2011. "Optimal combination forecasts for hierarchical time series," Computational Statistics & Data Analysis, Elsevier, vol. 55(9), pages 2579-2589, September.
    14. Kourentzes, Nikolaos & Petropoulos, Fotios & Trapero, Juan R., 2014. "Improving forecasting by estimating time series structural components across multiple frequencies," International Journal of Forecasting, Elsevier, vol. 30(2), pages 291-302.
    15. Tendai Makoni & Delson Chikobvu, 2023. "Assessing and Forecasting the Long-Term Impact of the Global Financial Crisis on Manufacturing Sales in South Africa," Economies, MDPI, vol. 11(6), pages 1-17, May.
    16. Fijorek Kamil & Leśniewska Agnieszka, 2012. "Statistical Forecasting of the Indicators of Polish Airport’s Operations," Folia Oeconomica Stetinensia, Sciendo, vol. 11(1), pages 7-7, January.
    17. Alysha M De Livera, 2010. "Automatic forecasting with a modified exponential smoothing state space framework," Monash Econometrics and Business Statistics Working Papers 10/10, Monash University, Department of Econometrics and Business Statistics.
    18. Spiliotis, Evangelos & Makridakis, Spyros & Kaltsounis, Anastasios & Assimakopoulos, Vassilios, 2021. "Product sales probabilistic forecasting: An empirical evaluation using the M5 competition data," International Journal of Production Economics, Elsevier, vol. 240(C).
    19. Miroslav Navratil & Andrea Kolkova, 2019. "Decomposition and Forecasting Time Series in the Business Economy Using Prophet Forecasting Model," Central European Business Review, Prague University of Economics and Business, vol. 2019(4), pages 26-39.
    20. G'abor Petneh'azi & J'ozsef G'all, 2019. "Mortality rate forecasting: can recurrent neural networks beat the Lee-Carter model?," Papers 1909.05501, arXiv.org, revised Oct 2019.

    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:jeners:v:13:y:2020:i:10:p:2578-:d:360302. 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.