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Patrick Eugene McSharry

Personal Details

First Name:Patrick
Middle Name:Eugene
Last Name:McSharry
Suffix:
RePEc Short-ID:pmc56
http://www.mcsharry.net

Affiliation

(in no particular order)

University of Oxford, Department of Engineering Science

http://www.eng.ox.ac.uk/
UK, Oxford

University of Oxford, Mathematical Institute

http://www.maths.ox.ac.uk
Oxford, UK

Research output

as
Jump to: Articles

Articles

  1. Njuguna, Christopher & McSharry, Patrick, 2017. "Constructing spatiotemporal poverty indices from big data," Journal of Business Research, Elsevier, vol. 70(C), pages 318-327.
  2. Georgios Anastasiades & Patrick McSharry, 2013. "Quantile Forecasting of Wind Power Using Variability Indices," Energies, MDPI, vol. 6(2), pages 1-34, February.
  3. Arora Siddharth & Little Max A. & McSharry Patrick E., 2013. "Nonlinear and nonparametric modeling approaches for probabilistic forecasting of the US gross national product," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 17(4), pages 395-420, September.
  4. Patrick McSharry, 2012. "Stream Analytics for Forecasting," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 24, pages 7-12, Winter.
  5. McSharry, Patrick E., 2011. "Validation and forecasting accuracy in models of climate change: Comments," International Journal of Forecasting, Elsevier, vol. 27(4), pages 996-999, October.
  6. David Orrell & Patrick McSharry, 2009. "Reply to Commentaries," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 14, pages 1-39, Summer.
  7. David Orrell & Patrick McSharry, 2009. "A Systems Approach to Forecasting," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 14, pages 25-30, Summer.
  8. Orrell, David & McSharry, Patrick, 2009. "System economics: Overcoming the pitfalls of forecasting models via a multidisciplinary approach," International Journal of Forecasting, Elsevier, vol. 25(4), pages 734-743, October.
  9. Taylor, James W. & de Menezes, Lilian M. & McSharry, Patrick E., 2006. "A comparison of univariate methods for forecasting electricity demand up to a day ahead," International Journal of Forecasting, Elsevier, vol. 22(1), pages 1-16.

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Articles

  1. Njuguna, Christopher & McSharry, Patrick, 2017. "Constructing spatiotemporal poverty indices from big data," Journal of Business Research, Elsevier, vol. 70(C), pages 318-327.

    Cited by:

    1. Ola Hall & Mattias Ohlsson & Thortseinn Rognvaldsson, 2022. "Satellite Image and Machine Learning based Knowledge Extraction in the Poverty and Welfare Domain," Papers 2203.01068, arXiv.org.
    2. Ola Hall & Francis Dompae & Ibrahim Wahab & Fred Mawunyo Dzanku, 2023. "A review of machine learning and satellite imagery for poverty prediction: Implications for development research and applications," Journal of International Development, John Wiley & Sons, Ltd., vol. 35(7), pages 1753-1768, October.
    3. Dedy Rahman Wijaya & Ni Luh Putu Satyaning Pradnya Paramita & Ana Uluwiyah & Muhammad Rheza & Annisa Zahara & Dwi Rani Puspita, 2022. "Estimating city-level poverty rate based on e-commerce data with machine learning," Electronic Commerce Research, Springer, vol. 22(1), pages 195-221, March.
    4. Boto Ferreira, Mário & Costa Pinto, Diego & Maurer Herter, Márcia & Soro, Jerônimo & Vanneschi, Leonardo & Castelli, Mauro & Peres, Fernando, 2021. "Using artificial intelligence to overcome over-indebtedness and fight poverty," Journal of Business Research, Elsevier, vol. 131(C), pages 411-425.
    5. Simone Cecchini & Giovanni Savio & Varinia Tromben, 2022. "Mapping poverty rates in Chile with night lights and fractional multinomial models," Regional Science Policy & Practice, Wiley Blackwell, vol. 14(4), pages 850-876, August.
    6. Purva Grover & Arpan Kumar Kar, 2017. "Big Data Analytics: A Review on Theoretical Contributions and Tools Used in Literature," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 18(3), pages 203-229, September.
    7. Jessica E. Steele & Carla Pezzulo & Maximilian Albert & Christopher J. Brooks & Elisabeth zu Erbach-Schoenberg & Siobhán B. O’Connor & Pål R. Sundsøy & Kenth Engø-Monsen & Kristine Nilsen & Bonita Gra, 2021. "Mobility and phone call behavior explain patterns in poverty at high-resolution across multiple settings," Palgrave Communications, Palgrave Macmillan, vol. 8(1), pages 1-12, December.
    8. Simon Lange & Utz Johann Pape & Peter Pütz, 2022. "Small Area Estimation of Poverty Under Structural Change," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 68(S2), pages 264-281, December.
    9. Gregorio Izquierdo Llanes & Antonio Salcedo Galiano, 2023. "Why does equivalization matter? An application to the monetary poverty in the sustainable development goals framework," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(3), pages 2575-2589, June.
    10. Chakraborty, Chiranjit & Joseph, Andreas, 2017. "Machine learning at central banks," Bank of England working papers 674, Bank of England.
    11. El-Haddadeh, Ramzi & Osmani, Mohamad & Hindi, Nitham & Fadlalla, Adam, 2021. "Value creation for realising the sustainable development goals: Fostering organisational adoption of big data analytics," Journal of Business Research, Elsevier, vol. 131(C), pages 402-410.
    12. McBride, Linden & Barrett, Christopher B. & Browne, Christopher & Hu, Leiqiu & Liu, Yanyan & Matteson, David S. & Sun, Ying & Wen, Jiaming, 2021. "Predicting poverty and malnutrition for targeting, mapping, monitoring, and early warning," 2021 Allied Social Sciences Association (ASSA) Annual Meeting (Virtual), January 3-5, 2021, San Diego, California 309060, Agricultural and Applied Economics Association.
    13. Akyildirim, Erdinc & Sensoy, Ahmet & Gulay, Guzhan & Corbet, Shaen & Salari, Hajar Novin, 2021. "Big data analytics, order imbalance and the predictability of stock returns," Journal of Multinational Financial Management, Elsevier, vol. 62(C).
    14. McSharry, Patrick & Mawejje, Joseph, 2024. "Estimating urban GDP growth using nighttime lights and machine learning techniques in data poor environments: The case of South Sudan," Technological Forecasting and Social Change, Elsevier, vol. 203(C).
    15. Darrold Cordes & Shahram Latifi & Gregory M. Morrison, 2022. "Systematic literature review of the performance characteristics of Chebyshev polynomials in machine learning applications for economic forecasting in low-income communities in sub-Saharan Africa," SN Business & Economics, Springer, vol. 2(12), pages 1-33, December.
    16. Yongming Xu & Yaping Mo & Shanyou Zhu, 2021. "Poverty Mapping in the Dian-Gui-Qian Contiguous Extremely Poor Area of Southwest China Based on Multi-Source Geospatial Data," Sustainability, MDPI, vol. 13(16), pages 1-14, August.

  2. Georgios Anastasiades & Patrick McSharry, 2013. "Quantile Forecasting of Wind Power Using Variability Indices," Energies, MDPI, vol. 6(2), pages 1-34, February.

    Cited by:

    1. Zhang, Yao & Wang, Jianxue & Wang, Xifan, 2014. "Review on probabilistic forecasting of wind power generation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 32(C), pages 255-270.
    2. Ying-Yi Hong & Ti-Hsuan Yu & Ching-Yun Liu, 2013. "Hour-Ahead Wind Speed and Power Forecasting Using Empirical Mode Decomposition," Energies, MDPI, vol. 6(12), pages 1-16, November.
    3. Ricardo J. Bessa & Corinna Möhrlen & Vanessa Fundel & Malte Siefert & Jethro Browell & Sebastian Haglund El Gaidi & Bri-Mathias Hodge & Umit Cali & George Kariniotakis, 2017. "Towards Improved Understanding of the Applicability of Uncertainty Forecasts in the Electric Power Industry," Energies, MDPI, vol. 10(9), pages 1-48, September.
    4. Gallego-Castillo, Cristobal & Bessa, Ricardo & Cavalcante, Laura & Lopez-Garcia, Oscar, 2016. "On-line quantile regression in the RKHS (Reproducing Kernel Hilbert Space) for operational probabilistic forecasting of wind power," Energy, Elsevier, vol. 113(C), pages 355-365.
    5. Arrieta-Prieto, Mario & Schell, Kristen R., 2022. "Spatio-temporal probabilistic forecasting of wind power for multiple farms: A copula-based hybrid model," International Journal of Forecasting, Elsevier, vol. 38(1), pages 300-320.
    6. Shen, Zhiwei & Ritter, Matthias, 2015. "Forecasting volatility of wind power production," SFB 649 Discussion Papers 2015-026, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

  3. Arora Siddharth & Little Max A. & McSharry Patrick E., 2013. "Nonlinear and nonparametric modeling approaches for probabilistic forecasting of the US gross national product," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 17(4), pages 395-420, September.

    Cited by:

    1. Mehmet Balcilar & Rangan Gupta & Anandamayee Majumdar & Stephen M. Miller, 2015. "Was the recent downturn in US real GDP predictable?," Applied Economics, Taylor & Francis Journals, vol. 47(28), pages 2985-3007, June.
    2. Berg Tim Oliver, 2017. "Forecast accuracy of a BVAR under alternative specifications of the zero lower bound," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 21(2), pages 1-29, April.
    3. Radek DOSKOČIL & Karel DOUBRAVSKÝ, 2017. "Qualitative Evaluation of Knowledge Based Model of Project Time-Cost as Decision Making Support," ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH, Faculty of Economic Cybernetics, Statistics and Informatics, vol. 51(1), pages 263-280.
    4. Giovanni Ballarin & Petros Dellaportas & Lyudmila Grigoryeva & Marcel Hirt & Sophie van Huellen & Juan-Pablo Ortega, 2022. "Reservoir Computing for Macroeconomic Forecasting with Mixed Frequency Data," Papers 2211.00363, arXiv.org, revised Jan 2024.
    5. Shaun P Vahey & Elizabeth C Wakerly, 2013. "Moving towards probability forecasting," BIS Papers chapters, in: Bank for International Settlements (ed.), Globalisation and inflation dynamics in Asia and the Pacific, volume 70, pages 3-8, Bank for International Settlements.
    6. Grabowski Daniel & Staszewska-Bystrova Anna & Winker Peter, 2017. "Generating prediction bands for path forecasts from SETAR models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 21(5), pages 1-18, December.
    7. Mehmet Balcilar & Rangan Gupta & Renee van Eyden & Kirsten Thompson, 2015. "Comparing the Forecasting Ability of Financial Conditions Indices: The Case of South Africa," Working Papers 15-06, Eastern Mediterranean University, Department of Economics.
    8. Mehmet Balcilar & Rangan Gupta & Anandamayee Majumdar & Stephen M. Miller, 2012. "Was the Recent Downturn in US GDP Predictable?," Working Papers 201230, University of Pretoria, Department of Economics.
    9. Xiuying Ma & Yongjing Wang & Haiyan Song & Han Liu, 2020. "Time-varying mechanisms between foreign direct investment and tourism development under the new normal in China," Tourism Economics, , vol. 26(2), pages 324-343, March.
    10. Lan Bai & Xiafei Li & Yu Wei & Guiwu Wei, 2022. "Does crude oil futures price really help to predict spot oil price? New evidence from density forecasting," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(3), pages 3694-3712, July.

  4. McSharry, Patrick E., 2011. "Validation and forecasting accuracy in models of climate change: Comments," International Journal of Forecasting, Elsevier, vol. 27(4), pages 996-999, October.

    Cited by:

    1. Hassani, Hossein & Silva, Emmanuel Sirimal & Gupta, Rangan & Das, Sonali, 2018. "Predicting global temperature anomaly: A definitive investigation using an ensemble of twelve competing forecasting models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 121-139.

  5. Orrell, David & McSharry, Patrick, 2009. "System economics: Overcoming the pitfalls of forecasting models via a multidisciplinary approach," International Journal of Forecasting, Elsevier, vol. 25(4), pages 734-743, October.

    Cited by:

    1. Makridakis, Spyros & Taleb, Nassim, 2009. "Decision making and planning under low levels of predictability," International Journal of Forecasting, Elsevier, vol. 25(4), pages 716-733, October.
    2. Peter Nielsen & Liping Jiang & Niels Gorm Malý Rytter & Gang Chen, 2014. "An investigation of forecast horizon and observation fit's influence on an econometric rate forecast model in the liner shipping industry," Maritime Policy & Management, Taylor & Francis Journals, vol. 41(7), pages 667-682, December.
    3. -, 2011. "An assessment of the economic impact of climate change on the tourism sector In Barbados," Sede Subregional de la CEPAL para el Caribe (Estudios e Investigaciones) 38602, Naciones Unidas Comisión Económica para América Latina y el Caribe (CEPAL).
    4. Arvydas Jadevicius & Brian Sloan & Andrew Brown, 2013. "Property Market Modelling and Forecasting: A Case for Simplicity," ERES eres2013_10, European Real Estate Society (ERES).
    5. David Blockley, 2023. "Exchanging Obligations: Accounting for All Forms of Capital," Journal of Interdisciplinary Economics, , vol. 35(1), pages 7-28, January.
    6. Dohnal, Mirko, 2016. "Complex biofuels related scenarios generated by qualitative reasoning under severe information shortages: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 65(C), pages 676-684.
    7. Wright, George & Goodwin, Paul, 2009. "Decision making and planning under low levels of predictability: Enhancing the scenario method," International Journal of Forecasting, Elsevier, vol. 25(4), pages 813-825, October.
    8. Doubravsky, Karel & Dohnal, Mirko, 2018. "Qualitative equationless macroeconomic models as generators of all possible forecasts based on three trend values—Increasing, constant, decreasing," Structural Change and Economic Dynamics, Elsevier, vol. 45(C), pages 30-36.
    9. Olga Kiuila, 2011. "Interactions between trade and environmental policies in the Czech economy," Working Papers 2011-16, Faculty of Economic Sciences, University of Warsaw.
    10. Zanoli, Raffaele & Gambelli, Danilo & Vairo, Daniela, 2012. "Scenarios of the organic food market in Europe," Food Policy, Elsevier, vol. 37(1), pages 41-57.
    11. Prasad, Ravita D. & Bansal, R.C. & Raturi, Atul, 2014. "Multi-faceted energy planning: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 38(C), pages 686-699.
    12. Derbyshire, James & Wright, George, 2017. "Augmenting the intuitive logics scenario planning method for a more comprehensive analysis of causation," International Journal of Forecasting, Elsevier, vol. 33(1), pages 254-266.
    13. Jan Kwakkel & Gönenç Yücel, 2014. "An Exploratory Analysis of the Dutch Electricity System in Transition," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 5(4), pages 670-685, December.
    14. Roberto Savona & Marika Vezzoli, 2015. "Fitting and Forecasting Sovereign Defaults using Multiple Risk Signals," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 77(1), pages 66-92, February.
    15. David Orrell, 2017. "A Quantum Theory of Money and Value, Part 2: The Uncertainty Principle," Economic Thought, World Economics Association, vol. 6(2), pages 14-26, September.
    16. Makridakis, Spyros & Hogarth, Robin M. & Gaba, Anil, 2009. "Forecasting and uncertainty in the economic and business world," International Journal of Forecasting, Elsevier, vol. 25(4), pages 794-812, October.
    17. Arora Siddharth & Little Max A. & McSharry Patrick E., 2013. "Nonlinear and nonparametric modeling approaches for probabilistic forecasting of the US gross national product," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 17(4), pages 395-420, September.

  6. Taylor, James W. & de Menezes, Lilian M. & McSharry, Patrick E., 2006. "A comparison of univariate methods for forecasting electricity demand up to a day ahead," International Journal of Forecasting, Elsevier, vol. 22(1), pages 1-16.

    Cited by:

    1. Ohtsuka, Yoshihiro & Oga, Takashi & Kakamu, Kazuhiko, 2010. "Forecasting electricity demand in Japan: A Bayesian spatial autoregressive ARMA approach," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2721-2735, November.
    2. D J Pedregal & P C Young, 2008. "Development of improved adaptive approaches to electricity demand forecasting," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(8), pages 1066-1076, August.
    3. Barrow, Devon & Kourentzes, Nikolaos, 2018. "The impact of special days in call arrivals forecasting: A neural network approach to modelling special days," European Journal of Operational Research, Elsevier, vol. 264(3), pages 967-977.
    4. Mukherjee, Paramita & Coondoo, Dipankor & Lahiri, Poulomi, 2019. "Forecasting Hourly Prices in Indian Spot Electricity Market," MPRA Paper 103161, University Library of Munich, Germany.
    5. Reisen, Valdério A. & Zamprogno, Bartolomeu & Palma, Wilfredo & Arteche, Josu, 2014. "A semiparametric approach to estimate two seasonal fractional parameters in the SARFIMA model," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 98(C), pages 1-17.
    6. Carlo Fezzi & Valeria Fanghella, 2020. "Real-time estimation of the short-run impact of COVID-19 on economic activity using electricity market data," Papers 2007.03477, arXiv.org.
    7. Tanrisever, Fehmi & Derinkuyu, Kursad & Heeren, Michael, 2013. "Forecasting electricity infeed for distribution system networks: An analysis of the Dutch case," Energy, Elsevier, vol. 58(C), pages 247-257.
    8. Eran Raviv & Kees E. Bouwman & Dick van Dijk, 2013. "Forecasting Day-Ahead Electricity Prices: Utilizing Hourly Prices," Tinbergen Institute Discussion Papers 13-068/III, Tinbergen Institute.
    9. Ismail Shah & Hasnain Iftikhar & Sajid Ali & Depeng Wang, 2019. "Short-Term Electricity Demand Forecasting Using Components Estimation Technique," Energies, MDPI, vol. 12(13), pages 1-17, July.
    10. Kamal Chapagain & Somsak Kittipiyakul, 2018. "Performance Analysis of Short-Term Electricity Demand with Atmospheric Variables," Energies, MDPI, vol. 11(4), pages 1-34, April.
    11. Pielow, Amy & Sioshansi, Ramteen & Roberts, Matthew C., 2012. "Modeling short-run electricity demand with long-term growth rates and consumer price elasticity in commercial and industrial sectors," Energy, Elsevier, vol. 46(1), pages 533-540.
    12. Rubin, Ofir D. & Babcock, Bruce A., 2011. "A novel approach for modeling deregulated electricity markets," Energy Policy, Elsevier, vol. 39(5), pages 2711-2721, May.
    13. Taylor, James W., 2006. "Density forecasting for the efficient balancing of the generation and consumption of electricity," International Journal of Forecasting, Elsevier, vol. 22(4), pages 707-724.
    14. Sharifzadeh, Mahdi & Sikinioti-Lock, Alexandra & Shah, Nilay, 2019. "Machine-learning methods for integrated renewable power generation: A comparative study of artificial neural networks, support vector regression, and Gaussian Process Regression," Renewable and Sustainable Energy Reviews, Elsevier, vol. 108(C), pages 513-538.
    15. Batalla-Bejerano, Joan & Costa-Campi, Maria Teresa & Trujillo-Baute, Elisa, 2016. "Collateral effects of liberalisation: Metering, losses, load profiles and cost settlement in Spain’s electricity system," Energy Policy, Elsevier, vol. 94(C), pages 421-431.
    16. Alexios Lekidis & Elpiniki I. Papageorgiou, 2023. "Edge-Based Short-Term Energy Demand Prediction," Energies, MDPI, vol. 16(14), pages 1-20, July.
    17. Sulandari, Winita & Subanar, & Lee, Muhammad Hisyam & Rodrigues, Paulo Canas, 2020. "Indonesian electricity load forecasting using singular spectrum analysis, fuzzy systems and neural networks," Energy, Elsevier, vol. 190(C).
    18. -, 2011. "An assessment of the economic impact of climate change on the tourism sector In Barbados," Sede Subregional de la CEPAL para el Caribe (Estudios e Investigaciones) 38602, Naciones Unidas Comisión Económica para América Latina y el Caribe (CEPAL).
    19. Jaume Rosselló Nadal & Mohcine Bakhat, 2009. "A new approach to estimating tourism-induced electricity consumption," CRE Working Papers (Documents de treball del CRE) 2009/6, Centre de Recerca Econòmica (UIB ·"Sa Nostra").
    20. Eichler, M. & Grothe, O. & Manner, H. & Türk, D.D.T., 2012. "Modeling spike occurrences in electricity spot prices for forecasting," Research Memorandum 029, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
    21. Kohler, M. & Blond, N. & Clappier, A., 2016. "A city scale degree-day method to assess building space heating energy demands in Strasbourg Eurometropolis (France)," Applied Energy, Elsevier, vol. 184(C), pages 40-54.
    22. Carlo Fezzi & Valeria Fanghella, 2020. "Real-time estimation of the short-run impact of COVID-19 on economic activity using electricity market data," DEM Working Papers 2020/8, Department of Economics and Management.
    23. Rallapalli, Srinivasa Rao & Ghosh, Sajal, 2012. "Forecasting monthly peak demand of electricity in India—A critique," Energy Policy, Elsevier, vol. 45(C), pages 516-520.
    24. Chan, Kam Fong & Gray, Philip & van Campen, Bart, 2008. "A new approach to characterizing and forecasting electricity price volatility," International Journal of Forecasting, Elsevier, vol. 24(4), pages 728-743.
    25. Deb, Chirag & Zhang, Fan & Yang, Junjing & Lee, Siew Eang & Shah, Kwok Wei, 2017. "A review on time series forecasting techniques for building energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 902-924.
    26. James W. Taylor & Ralph D. Snyder, 2009. "Forecasting Intraday Time Series with Multiple Seasonal Cycles Using Parsimonious Seasonal Exponential Smoothing," Monash Econometrics and Business Statistics Working Papers 9/09, Monash University, Department of Econometrics and Business Statistics.
    27. Taylor, James W., 2010. "Exponentially weighted methods for forecasting intraday time series with multiple seasonal cycles," International Journal of Forecasting, Elsevier, vol. 26(4), pages 627-646, October.
    28. Zhou, Fan & Page, Lionel & Perrons, Robert K. & Zheng, Zuduo & Washington, Simon, 2019. "Long-term forecasts for energy commodities price: What the experts think," Energy Economics, Elsevier, vol. 84(C).
    29. Magnano, L. & Boland, J.W., 2007. "Generation of synthetic sequences of electricity demand: Application in South Australia," Energy, Elsevier, vol. 32(11), pages 2230-2243.
    30. Liang, Xin & Hong, Tianzhen & Shen, Geoffrey Qiping, 2016. "Improving the accuracy of energy baseline models for commercial buildings with occupancy data," Applied Energy, Elsevier, vol. 179(C), pages 247-260.
    31. Paraschiv, Florentina & Erni, David & Pietsch, Ralf, 2014. "The impact of renewable energies on EEX day-ahead electricity prices," Energy Policy, Elsevier, vol. 73(C), pages 196-210.
    32. Rafal Weron, 2006. "Modeling and Forecasting Electricity Loads and Prices: A Statistical Approach," HSC Books, Hugo Steinhaus Center, Wroclaw University of Science and Technology, number hsbook0601, December.
    33. Trapero, Juan R. & Pedregal, Diego J., 2009. "Frequency domain methods applied to forecasting electricity markets," Energy Economics, Elsevier, vol. 31(5), pages 727-735, September.
    34. Webel, Karsten, 2022. "A review of some recent developments in the modelling and seasonal adjustment of infra-monthly time series," Discussion Papers 31/2022, Deutsche Bundesbank.
    35. Goutam Dutta & Krishnendranath Mitra, 2017. "A literature review on dynamic pricing of electricity," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(10), pages 1131-1145, October.
    36. Lobo, Benjamin J. & Brown, Donald E. & Grazaitis, Peter J., 2019. "Long-term forecasting of fuel demand at theater entry points," International Journal of Forecasting, Elsevier, vol. 35(2), pages 502-520.
    37. Cristina Miranda & Reinaldo Castro Souza & Mônica Barros & Cristina Vidigal Cabral de Miranda, 2007. "Short Term Demand Forecasting Using Double Exponential Smoothing and Interventions to Account for Holidays and Temperature Effects," EcoMod2007 23900058, EcoMod.
    38. Hahn, Heiko & Meyer-Nieberg, Silja & Pickl, Stefan, 2009. "Electric load forecasting methods: Tools for decision making," European Journal of Operational Research, Elsevier, vol. 199(3), pages 902-907, December.
    39. Miloš Božić & Miloš Stojanović & Zoran Stajić & Dragan Tasić, 2013. "A New Two-Stage Approach to Short Term Electrical Load Forecasting," Energies, MDPI, vol. 6(4), pages 1-19, April.
    40. Karamaziotis, Panagiotis I. & Raptis, Achilleas & Nikolopoulos, Konstantinos & Litsiou, Konstantia & Assimakopoulos, Vassilis, 2020. "An empirical investigation of water consumption forecasting methods," International Journal of Forecasting, Elsevier, vol. 36(2), pages 588-606.
    41. V. Dordonnat & S.J. Koopman & M. Ooms & A. Dessertaine & J. Collet, 2008. "An Hourly Periodic State Space Model for Modelling French National Electricity Load," Tinbergen Institute Discussion Papers 08-008/4, Tinbergen Institute.
    42. Salah Bouktif & Ali Fiaz & Ali Ouni & Mohamed Adel Serhani, 2018. "Optimal Deep Learning LSTM Model for Electric Load Forecasting using Feature Selection and Genetic Algorithm: Comparison with Machine Learning Approaches †," Energies, MDPI, vol. 11(7), pages 1-20, June.
    43. Walter, Travis & Price, Phillip N. & Sohn, Michael D., 2014. "Uncertainty estimation improves energy measurement and verification procedures," Applied Energy, Elsevier, vol. 130(C), pages 230-236.
    44. Motlagh, Omid & Paevere, Phillip & Hong, Tang Sai & Grozev, George, 2015. "Analysis of household electricity consumption behaviours: Impact of domestic electricity generation," Applied Mathematics and Computation, Elsevier, vol. 270(C), pages 165-178.
    45. Silva, Hendrigo Batista da & Santiago, Leonardo P., 2018. "On the trade-off between real-time pricing and the social acceptability costs of demand response," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1513-1521.
    46. Tine Van Calster & Filip Van den Bossche & Bart Baesens & Wilfried Lemahieu, 2020. "Profit-oriented sales forecasting: a comparison of forecasting techniques from a business perspective," Papers 2002.00949, arXiv.org.
    47. Taylor, James W., 2008. "An evaluation of methods for very short-term load forecasting using minute-by-minute British data," International Journal of Forecasting, Elsevier, vol. 24(4), pages 645-658.
    48. Cancelo, José Ramón & Grafe, Rosmarie, 2007. "Forecasting from one day to one week ahead for the Spanish system operator," DES - Working Papers. Statistics and Econometrics. WS ws078418, Universidad Carlos III de Madrid. Departamento de Estadística.
    49. Rendon-Sanchez, Juan F. & de Menezes, Lilian M., 2019. "Structural combination of seasonal exponential smoothing forecasts applied to load forecasting," European Journal of Operational Research, Elsevier, vol. 275(3), pages 916-924.
    50. Smith, Michael Stanley & Shively, Thomas S., 2018. "Econometric modeling of regional electricity spot prices in the Australian market," Energy Economics, Elsevier, vol. 74(C), pages 886-903.
    51. Ayman A. Amin, 2020. "Bayesian Analysis of Double Seasonal Autoregressive Models," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 82(2), pages 328-352, November.
    52. Niematallah Elamin & Mototsugu Fukushige, 2017. "The 2011 Japanese energy crisis: Effects on the magnitude and pattern of load demand," Discussion Papers in Economics and Business 17-19, Osaka University, Graduate School of Economics.
    53. Salah Bouktif & Ali Fiaz & Ali Ouni & Mohamed Adel Serhani, 2019. "Single and Multi-Sequence Deep Learning Models for Short and Medium Term Electric Load Forecasting," Energies, MDPI, vol. 12(1), pages 1-21, January.
    54. Elamin, Niematallah & Fukushige, Mototsugu, 2018. "Modeling and forecasting hourly electricity demand by SARIMAX with interactions," Energy, Elsevier, vol. 165(PB), pages 257-268.
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