IDEAS home Printed from https://ideas.repec.org/f/pso695.html
   My authors  Follow this author

Emmanouil Sofianos

Personal Details

First Name:Emmanouil
Middle Name:
Last Name:Sofianos
Suffix:
RePEc Short-ID:pso695
[This author has chosen not to make the email address public]
http://esofianos.gr/

Affiliation

Bureau d'Économie Théorique et Appliquée (BETA)

Nancy/Strasbourg, France
https://www.beta-economics.fr/
RePEc:edi:bestrfr (more details at EDIRC)

Research output

as
Jump to: Working papers Articles

Working papers

  1. Gogas, Periklis & Papadimitriou, Theophilos & Sofianos, Emmanouil, 2019. "Money Neutrality, Monetary Aggregates and Machine Learning," DUTH Research Papers in Economics 4-2016, Democritus University of Thrace, Department of Economics.

Articles

  1. Emmanouil Sofianos & Emmanouil Zaganidis & Theophilos Papadimitriou & Periklis Gogas, 2024. "Forecasting East and West Coast Gasoline Prices with Tree-Based Machine Learning Algorithms," Energies, MDPI, vol. 17(6), pages 1-14, March.
  2. Monica Alexiadou & Emmanouil Sofianos & Periklis Gogas & Theophilos Papadimitriou, 2023. "Cryptocurrencies and Long-Range Trends," IJFS, MDPI, vol. 11(1), pages 1-17, February.
  3. Periklis Gogas & Theophilos Papadimitriou & Emmanouil Sofianos, 2022. "Forecasting unemployment in the euro area with machine learning," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(3), pages 551-566, April.
  4. Emmanouil Sofianos & Periklis Gogas & Theophilos Papadimitriou, 2022. "Mind the gap: forecasting euro-area output gaps with machine learning," Applied Economics Letters, Taylor & Francis Journals, vol. 29(19), pages 1824-1828, November.
  5. Dimitrios Mouchtaris & Emmanouil Sofianos & Periklis Gogas & Theophilos Papadimitriou, 2021. "Forecasting Natural Gas Spot Prices with Machine Learning," Energies, MDPI, vol. 14(18), pages 1-13, September.

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.

Working papers

  1. Gogas, Periklis & Papadimitriou, Theophilos & Sofianos, Emmanouil, 2019. "Money Neutrality, Monetary Aggregates and Machine Learning," DUTH Research Papers in Economics 4-2016, Democritus University of Thrace, Department of Economics.

    Cited by:

    1. Dimitrios Mouchtaris & Emmanouil Sofianos & Periklis Gogas & Theophilos Papadimitriou, 2021. "Forecasting Natural Gas Spot Prices with Machine Learning," Energies, MDPI, vol. 14(18), pages 1-13, September.
    2. Lu, Yao & Zhao, Zhihui & Tian, Yuan & Zhan, Minghua, 2024. "How does the economic structure break change the forecast effect of money and credit on output? Evidence based on machine learning algorithms," Pacific-Basin Finance Journal, Elsevier, vol. 84(C).
    3. Emmanouil Sofianos & Emmanouil Zaganidis & Theophilos Papadimitriou & Periklis Gogas, 2024. "Forecasting East and West Coast Gasoline Prices with Tree-Based Machine Learning Algorithms," Energies, MDPI, vol. 17(6), pages 1-14, March.
    4. Periklis Gogas & Theophilos Papadimitriou & Emmanouil Sofianos, 2022. "Forecasting unemployment in the euro area with machine learning," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(3), pages 551-566, April.

Articles

  1. Periklis Gogas & Theophilos Papadimitriou & Emmanouil Sofianos, 2022. "Forecasting unemployment in the euro area with machine learning," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(3), pages 551-566, April.

    Cited by:

    1. Labib Shami & Teddy Lazebnik, 2024. "Implementing Machine Learning Methods in Estimating the Size of the Non-observed Economy," Computational Economics, Springer;Society for Computational Economics, vol. 63(4), pages 1459-1476, April.
    2. Chris Reimann, 2024. "Predicting financial crises: an evaluation of machine learning algorithms and model explainability for early warning systems," Review of Evolutionary Political Economy, Springer, vol. 5(1), pages 51-83, June.
    3. Sanusi, Olajide I. & Safi, Samir K. & Adeeko, Omotara & Tabash, Mosab I., 2022. "Forecasting agricultural commodity price using different models: a case study of widely consumed grains in Nigeria," Agricultural and Resource Economics: International Scientific E-Journal, Agricultural and Resource Economics: International Scientific E-Journal, vol. 8(2), June.
    4. Mustafa Yurtsever, 2023. "Unemployment rate forecasting: LSTM-GRU hybrid approach," Journal for Labour Market Research, Springer;Institute for Employment Research/ Institut für Arbeitsmarkt- und Berufsforschung (IAB), vol. 57(1), pages 1-9, December.
    5. Efstathios Polyzos & Costas Siriopoulos, 2024. "Autoregressive Random Forests: Machine Learning and Lag Selection for Financial Research," Computational Economics, Springer;Society for Computational Economics, vol. 64(1), pages 225-262, July.
    6. Kea Baret & Amélie Barbier-Gauchard & Theophilos Papadimitriou, 2023. "Forecasting the Stability and Growth Pact compliance using Machine Learning," Post-Print hal-03121966, HAL.
    7. Mirza, Nawazish & Rizvi, Syed Kumail Abbas & Naqvi, Bushra & Umar, Muhammad, 2024. "Inflation prediction in emerging economies: Machine learning and FX reserves integration for enhanced forecasting," International Review of Financial Analysis, Elsevier, vol. 94(C).

  2. Emmanouil Sofianos & Periklis Gogas & Theophilos Papadimitriou, 2022. "Mind the gap: forecasting euro-area output gaps with machine learning," Applied Economics Letters, Taylor & Francis Journals, vol. 29(19), pages 1824-1828, November.

    Cited by:

    1. Mustafa Yurtsever, 2023. "Unemployment rate forecasting: LSTM-GRU hybrid approach," Journal for Labour Market Research, Springer;Institute for Employment Research/ Institut für Arbeitsmarkt- und Berufsforschung (IAB), vol. 57(1), pages 1-9, December.

  3. Dimitrios Mouchtaris & Emmanouil Sofianos & Periklis Gogas & Theophilos Papadimitriou, 2021. "Forecasting Natural Gas Spot Prices with Machine Learning," Energies, MDPI, vol. 14(18), pages 1-13, September.

    Cited by:

    1. Renchu Guan & Aoqing Wang & Yanchun Liang & Jiasheng Fu & Xiaosong Han, 2022. "International Natural Gas Price Trends Prediction with Historical Prices and Related News," Energies, MDPI, vol. 15(10), pages 1-14, May.
    2. Sun-Feel Yang & So-Won Choi & Eul-Bum Lee, 2023. "A Prediction Model for Spot LNG Prices Based on Machine Learning Algorithms to Reduce Fluctuation Risks in Purchasing Prices," Energies, MDPI, vol. 16(11), pages 1-39, May.
    3. Yadong Pei & Chiou-Jye Huang & Yamin Shen & Mingyue Wang, 2023. "A Novel Model for Spot Price Forecast of Natural Gas Based on Temporal Convolutional Network," Energies, MDPI, vol. 16(5), pages 1-15, February.
    4. Periklis Gogas & Theophilos Papadimitriou, 2022. "Emerging Trends in Energy Economics," Energies, MDPI, vol. 15(14), pages 1-2, July.

More information

Research fields, statistics, top rankings, if available.

Statistics

Access and download statistics for all items

Co-authorship network on CollEc

NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 1 paper announced in NEP. These are the fields, ordered by number of announcements, along with their dates. If the author is listed in the directory of specialists for this field, a link is also provided.
  1. NEP-BIG: Big Data (1) 2019-11-18. Author is listed
  2. NEP-CBA: Central Banking (1) 2019-11-18. Author is listed
  3. NEP-CMP: Computational Economics (1) 2019-11-18. Author is listed
  4. NEP-EEC: European Economics (1) 2019-11-18. Author is listed
  5. NEP-FOR: Forecasting (1) 2019-11-18. Author is listed
  6. NEP-MAC: Macroeconomics (1) 2019-11-18. Author is listed
  7. NEP-MON: Monetary Economics (1) 2019-11-18. Author is listed

Corrections

All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. For general information on how to correct material on RePEc, see these instructions.

To update listings or check citations waiting for approval, Emmanouil Sofianos should log into the RePEc Author Service.

To make corrections to the bibliographic information of a particular item, find the technical contact on the abstract page of that item. There, details are also given on how to add or correct references and citations.

To link different versions of the same work, where versions have a different title, use this form. Note that if the versions have a very similar title and are in the author's profile, the links will usually be created automatically.

Please note that most corrections can 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.