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Forecasting Economics and Financial Time Series: ARIMA vs. LSTM

Citations

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Cited by:

  1. Mario Zupan, 2024. "Accounting journal entries as a long‐term multivariate time series: Forecasting wholesale warehouse output," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 31(1), March.
  2. Aniruddha Dutta & Saket Kumar & Meheli Basu, 2019. "A Gated Recurrent Unit Approach to Bitcoin Price Prediction," Papers 1912.11166, arXiv.org.
  3. Samer Chaaraoui & Matthias Bebber & Stefanie Meilinger & Silvan Rummeny & Thorsten Schneiders & Windmanagda Sawadogo & Harald Kunstmann, 2021. "Day-Ahead Electric Load Forecast for a Ghanaian Health Facility Using Different Algorithms," Energies, MDPI, vol. 14(2), pages 1-22, January.
  4. Mourad Mroua & Ahlem Lamine, 2023. "Financial time series prediction under Covid-19 pandemic crisis with Long Short-Term Memory (LSTM) network," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-15, December.
  5. Aniruddha Dutta & Saket Kumar & Meheli Basu, 2020. "A Gated Recurrent Unit Approach to Bitcoin Price Prediction," JRFM, MDPI, vol. 13(2), pages 1-16, February.
  6. Sima Siami‐Namini & Darren Hudson & Adao Alexandre Trindade & Conrad Lyford, 2019. "Commodity price volatility and U.S. monetary policy: Commodity price overshooting revisited," Agribusiness, John Wiley & Sons, Ltd., vol. 35(2), pages 200-218, April.
  7. 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.
  8. Kevin Villalobos & Johan Suykens & Arantza Illarramendi, 2021. "A flexible alarm prediction system for smart manufacturing scenarios following a forecaster–analyzer approach," Journal of Intelligent Manufacturing, Springer, vol. 32(5), pages 1323-1344, June.
  9. Adamantios Ntakaris & Giorgio Mirone & Juho Kanniainen & Moncef Gabbouj & Alexandros Iosifidis, 2019. "Feature Engineering for Mid-Price Prediction with Deep Learning," Papers 1904.05384, arXiv.org, revised Jun 2019.
  10. Ghosh, Pushpendu & Neufeld, Ariel & Sahoo, Jajati Keshari, 2022. "Forecasting directional movements of stock prices for intraday trading using LSTM and random forests," Finance Research Letters, Elsevier, vol. 46(PA).
  11. Min Hu & Zhizhong Tan & Bin Liu & Guosheng Yin, 2023. "Futures Quantitative Investment with Heterogeneous Continual Graph Neural Network," Papers 2303.16532, arXiv.org, revised Dec 2023.
  12. Himanshu Gupta & Aditya Jaiswal, 2024. "A Study on Stock Forecasting Using Deep Learning and Statistical Models," Papers 2402.06689, arXiv.org.
  13. Pushpendu Ghosh & Ariel Neufeld & Jajati Keshari Sahoo, 2020. "Forecasting directional movements of stock prices for intraday trading using LSTM and random forests," Papers 2004.10178, arXiv.org, revised Jun 2021.
  14. Zongyu Li & Anmin Zuo & Cuixia Li, 2023. "Predicting Raw Milk Price Based on Depth Time Series Features for Consumer Behavior Analysis," Sustainability, MDPI, vol. 15(8), pages 1-15, April.
  15. Gun Il Kim & Beakcheol Jang, 2023. "Petroleum Price Prediction with CNN-LSTM and CNN-GRU Using Skip-Connection," Mathematics, MDPI, vol. 11(3), pages 1-16, January.
  16. Sima Siami-Namini & Daniel Muhammad & Fahad Fahimullah, 2018. "The Short and Long Run Effects of Selected Variables on Tax Revenue - A Case Study," Applied Economics and Finance, Redfame publishing, vol. 5(5), pages 23-32, September.
  17. Xiangzhou Chen & Zhi Long, 2023. "E-Commerce Enterprises Financial Risk Prediction Based on FA-PSO-LSTM Neural Network Deep Learning Model," Sustainability, MDPI, vol. 15(7), pages 1-17, March.
  18. Rayadurgam, Vikram Chandramouli & Mangalagiri, Jayasree, 2023. "Does inclusion of GARCH variance in deep learning models improve financial contagion prediction?," Finance Research Letters, Elsevier, vol. 54(C).
  19. Depren, Özer & Kartal, Mustafa Tevfik & Kılıç Depren, Serpil, 2021. "Changes of gold prices in COVID-19 pandemic: Daily evidence from Turkey's monetary policy measures with selected determinants," Technological Forecasting and Social Change, Elsevier, vol. 170(C).
  20. Vanshu Mahajan & Sunil Thakan & Aashish Malik, 2022. "Modeling and Forecasting the Volatility of NIFTY 50 Using GARCH and RNN Models," Economies, MDPI, vol. 10(5), pages 1-20, April.
  21. Sadefo Kamdem, Jules & Bandolo Essomba, Rose & Njong Berinyuy, James, 2020. "Deep learning models for forecasting and analyzing the implications of COVID-19 spread on some commodities markets volatilities," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
  22. Guoteng Xu & Shuai Peng & Chengjiang Li & Xia Chen, 2023. "Synergistic Evolution of China’s Green Economy and Digital Economy Based on LSTM-GM and Grey Absolute Correlation," Sustainability, MDPI, vol. 15(19), pages 1-29, September.
  23. Jireh Yi-Le Chan & Steven Mun Hong Leow & Khean Thye Bea & Wai Khuen Cheng & Seuk Wai Phoong & Zeng-Wei Hong & Jim-Min Lin & Yen-Lin Chen, 2022. "A Correlation-Embedded Attention Module to Mitigate Multicollinearity: An Algorithmic Trading Application," Mathematics, MDPI, vol. 10(8), pages 1-13, April.
  24. Masum, Mohammad & Masud, M.A. & Adnan, Muhaiminul Islam & Shahriar, Hossain & Kim, Sangil, 2022. "Comparative study of a mathematical epidemic model, statistical modeling, and deep learning for COVID-19 forecasting and management," Socio-Economic Planning Sciences, Elsevier, vol. 80(C).
  25. Ayush Jain & Smit Marvaniya & Shantanu Godbole & Vitobha Munigala, 2020. "A Framework for Crop Price Forecasting in Emerging Economies by Analyzing the Quality of Time-series Data," Papers 2009.04171, arXiv.org.
  26. Jonas Hanetho, 2023. "Deep Policy Gradient Methods in Commodity Markets," Papers 2308.01910, arXiv.org.
  27. Omer Berat Sezer & Mehmet Ugur Gudelek & Ahmet Murat Ozbayoglu, 2019. "Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019," Papers 1911.13288, arXiv.org.
  28. Montserrat Reyna Miranda & Ricardo Massa Roldán & Vicente Gómez Salcido, 2022. "Neuro-wavelet Model for price prediction in high-frequency data in the Mexican Stock market," Remef - Revista Mexicana de Economía y Finanzas Nueva Época REMEF (The Mexican Journal of Economics and Finance), Instituto Mexicano de Ejecutivos de Finanzas, IMEF, vol. 17(1), pages 1-23, Enero - M.
  29. de Lucio, Juan, 2021. "Estimación adelantada del crecimiento regional mediante redes neuronales LSTM," INVESTIGACIONES REGIONALES - Journal of REGIONAL RESEARCH, Asociación Española de Ciencia Regional, issue 49, pages 45-64.
  30. Yu-Tse Tsan & Der-Yuan Chen & Po-Yu Liu & Endah Kristiani & Kieu Lan Phuong Nguyen & Chao-Tung Yang, 2022. "The Prediction of Influenza-like Illness and Respiratory Disease Using LSTM and ARIMA," IJERPH, MDPI, vol. 19(3), pages 1-17, February.
  31. Li Long, Chan & Guleria, Yash & Alam, Sameer, 2021. "Air passenger forecasting using Neural Granger causal Google trend queries," Journal of Air Transport Management, Elsevier, vol. 95(C).
  32. Hyeongjun Kim & Hoon Cho & Doojin Ryu, 2022. "Corporate Bankruptcy Prediction Using Machine Learning Methodologies with a Focus on Sequential Data," Computational Economics, Springer;Society for Computational Economics, vol. 59(3), pages 1231-1249, March.
  33. Gyana Ranjan Patra & Mihir Narayan Mohanty, 2023. "Price Prediction of Cryptocurrency Using a Multi-Layer Gated Recurrent Unit Network with Multi Features," Computational Economics, Springer;Society for Computational Economics, vol. 62(4), pages 1525-1544, December.
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