An Integrated Variational Mode Decomposition and ARIMA Model to Forecast Air Temperature
Author
Abstract
Suggested Citation
Download full text from publisher
References listed on IDEAS
- E. A. G. Schuur & A. D. McGuire & C. Schädel & G. Grosse & J. W. Harden & D. J. Hayes & G. Hugelius & C. D. Koven & P. Kuhry & D. M. Lawrence & S. M. Natali & D. Olefeldt & V. E. Romanovsky & K. Schae, 2015. "Climate change and the permafrost carbon feedback," Nature, Nature, vol. 520(7546), pages 171-179, April.
- Yuan, Chaoqing & Liu, Sifeng & Fang, Zhigeng, 2016. "Comparison of China's primary energy consumption forecasting by using ARIMA (the autoregressive integrated moving average) model and GM(1,1) model," Energy, Elsevier, vol. 100(C), pages 384-390.
- Myles R. Allen & David J. Frame & Chris Huntingford & Chris D. Jones & Jason A. Lowe & Malte Meinshausen & Nicolai Meinshausen, 2009. "Warming caused by cumulative carbon emissions towards the trillionth tonne," Nature, Nature, vol. 458(7242), pages 1163-1166, April.
- Paulo, A.A. & Ferreira, E. & Coelho, C. & Pereira, L.S., 2005. "Drought class transition analysis through Markov and Loglinear models, an approach to early warning," Agricultural Water Management, Elsevier, vol. 77(1-3), pages 59-81, August.
- Shukur, Osamah Basheer & Lee, Muhammad Hisyam, 2015. "Daily wind speed forecasting through hybrid KF-ANN model based on ARIMA," Renewable Energy, Elsevier, vol. 76(C), pages 637-647.
- Lee R. Kump, 2000. "What drives climate?," Nature, Nature, vol. 408(6813), pages 651-652, December.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Long Qian & Lifeng Wu & Xiaogang Liu & Yaokui Cui & Yongwen Wang, 2022. "Comparison of CLDAS and Machine Learning Models for Reference Evapotranspiration Estimation under Limited Meteorological Data," Sustainability, MDPI, vol. 14(21), pages 1-24, November.
- Syed Naeem Haider & Qianchuan Zhao & Xueliang Li, 2020. "Cluster-Based Prediction for Batteries in Data Centers," Energies, MDPI, vol. 13(5), pages 1-17, March.
- Atif Maqbool Khan & Magdalena Osińska, 2021. "How to Predict Energy Consumption in BRICS Countries?," Energies, MDPI, vol. 14(10), pages 1-21, May.
- Shuai Han & Buchun Liu & Chunxiang Shi & Yuan Liu & Meijuan Qiu & Shuai Sun, 2020. "Evaluation of CLDAS and GLDAS Datasets for Near-Surface Air Temperature over Major Land Areas of China," Sustainability, MDPI, vol. 12(10), pages 1-19, May.
- Pruethsan Sutthichaimethee & Sthianrapab Naluang, 2019. "The Efficiency of the Sustainable Development Policy for Energy Consumption under Environmental Law in Thailand: Adapting the SEM-VARIMAX Model," Energies, MDPI, vol. 12(16), pages 1-21, August.
- Weiwei Lin & Yanping Shi, 2023. "A Study on the Development of China’s Financial Leasing Industry Based on Principal Component Analysis and ARIMA Model," Sustainability, MDPI, vol. 15(13), pages 1-20, June.
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.- Rickels, Wilfried & Merk, Christine & Honneth, Johannes & Schwinger, Jörg & Quaas, Martin F. & Oschlies, Andreas, 2019. "Welche Rolle spielen negative Emissionen für die zukünftige Klimapolitik? Eine ökonomische Einschätzung des 1,5°C-Sonderberichts des Weltklimarats," Kiel Working Papers 2116, Kiel Institute for the World Economy (IfW Kiel).
- García, Irene & Huo, Stella & Prado, Raquel & Bravo, Lelys, 2020. "Dynamic Bayesian temporal modeling and forecasting of short-term wind measurements," Renewable Energy, Elsevier, vol. 161(C), pages 55-64.
- Li, Min & Yang, Yi & He, Zhaoshuang & Guo, Xinbo & Zhang, Ruisheng & Huang, Bingqing, 2023. "A wind speed forecasting model based on multi-objective algorithm and interpretability learning," Energy, Elsevier, vol. 269(C).
- Liu, Xiaolei & Lin, Zi & Feng, Ziming, 2021. "Short-term offshore wind speed forecast by seasonal ARIMA - A comparison against GRU and LSTM," Energy, Elsevier, vol. 227(C).
- Dietz, Simon & Gollier, Christian & Kessler, Louise, 2018.
"The climate beta,"
Journal of Environmental Economics and Management, Elsevier, vol. 87(C), pages 258-274.
- Simon Dietz & Christian Gollier & Louise Kessler, 2015. "The climate beta," GRI Working Papers 190, Grantham Research Institute on Climate Change and the Environment.
- Dietz, Simon & Gollier, Christian & Kessler, Louise, 2015. "The climate beta," TSE Working Papers 15-608, Toulouse School of Economics (TSE).
- Dietz, Simon & Gollier, Christian & Kessler, Louise, 2018. "The climate beta," LSE Research Online Documents on Economics 83605, London School of Economics and Political Science, LSE Library.
- Dietz, Simon & Gollier, Christian & Kessler, Louise, 2015. "The climate beta," IDEI Working Papers 856, Institut d'Économie Industrielle (IDEI), Toulouse.
- Pin Li & Jinsuo Zhang, 2019. "Is China’s Energy Supply Sustainable? New Research Model Based on the Exponential Smoothing and GM(1,1) Methods," Energies, MDPI, vol. 12(2), pages 1-30, January.
- Hoel, Michael, 2016. "Optimal control theory with applications to resource and environmental economics," Memorandum 08/2016, Oslo University, Department of Economics.
- Ding, Song & Tao, Zui & Zhang, Huahan & Li, Yao, 2022. "Forecasting nuclear energy consumption in China and America: An optimized structure-adaptative grey model," Energy, Elsevier, vol. 239(PA).
- Gustav Engström & Johan Gars, 2016. "Climatic Tipping Points and Optimal Fossil-Fuel Use," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 65(3), pages 541-571, November.
- Javad Bazrafshan & Somayeh Hejabi & Jaber Rahimi, 2014. "Drought Monitoring Using the Multivariate Standardized Precipitation Index (MSPI)," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(4), pages 1045-1060, March.
- Ke Yan & Xudong Wang & Yang Du & Ning Jin & Haichao Huang & Hangxia Zhou, 2018. "Multi-Step Short-Term Power Consumption Forecasting with a Hybrid Deep Learning Strategy," Energies, MDPI, vol. 11(11), pages 1-15, November.
- Linnenluecke, Martina K. & Smith, Tom & McKnight, Brent, 2016. "Environmental finance: A research agenda for interdisciplinary finance research," Economic Modelling, Elsevier, vol. 59(C), pages 124-130.
- Schaeffer, Michiel & Gohar, Laila & Kriegler, Elmar & Lowe, Jason & Riahi, Keywan & van Vuuren, Detlef, 2015. "Mid- and long-term climate projections for fragmented and delayed-action scenarios," Technological Forecasting and Social Change, Elsevier, vol. 90(PA), pages 257-268.
- Wang, Kejun & Qi, Xiaoxia & Liu, Hongda & Song, Jiakang, 2018. "Deep belief network based k-means cluster approach for short-term wind power forecasting," Energy, Elsevier, vol. 165(PA), pages 840-852.
- Adam Michael Bauer & Cristian Proistosescu & Gernot Wagner, 2023. "Carbon Dioxide as a Risky Asset," CESifo Working Paper Series 10278, CESifo.
- Suat Ozturk & Feride Ozturk, 2018. "Forecasting Energy Consumption of Turkey by Arima Model," Journal of Asian Scientific Research, Asian Economic and Social Society, vol. 8(2), pages 52-60, February.
- Xiangwen Wu & Shuying Zang & Dalong Ma & Jianhua Ren & Qiang Chen & Xingfeng Dong, 2019. "Emissions of CO 2 , CH 4 , and N 2 O Fluxes from Forest Soil in Permafrost Region of Daxing’an Mountains, Northeast China," IJERPH, MDPI, vol. 16(16), pages 1-14, August.
- Sen, Suphi & von Schickfus, Marie-Theres, 2020.
"Climate policy, stranded assets, and investors’ expectations,"
Journal of Environmental Economics and Management, Elsevier, vol. 100(C).
- Suphi Sen & Marie-Theres von Schickfus, 2019. "Climate Policy, Stranded Assets, and Investors' Expectations," CESifo Working Paper Series 7945, CESifo.
- Sen, Suphi & Schickfus, Marie-Theres von, 2020. "Climate policy, stranded assets, and investors expectations," Munich Reprints in Economics 84748, University of Munich, Department of Economics.
- Frederick Ploeg, 2021. "Carbon pricing under uncertainty," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 28(5), pages 1122-1142, October.
- Seiichi KATAYAMA & Ngo Van LONG & Hiroshi OHTA, 2013. "Carbon Taxes in a Trading World," GSICS Working Paper Series 26, Graduate School of International Cooperation Studies, Kobe University.
More about this item
Keywords
climate change; forecasting model; VMD; ARIMA; feature mining; Wuhan;All these keywords.
Statistics
Access and download statisticsCorrections
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:jsusta:v:11:y:2019:i:15:p:4018-:d:251446. 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.