IDEAS home Printed from https://ideas.repec.org/r/arx/papers/1803.06386.html
   My bibliography  Save this item

Forecasting Economics and Financial Time Series: ARIMA vs. LSTM

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


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. Wang, Yijun & Andreeva, Galina & Martin-Barragan, Belen, 2023. "Machine learning approaches to forecasting cryptocurrency volatility: Considering internal and external determinants," International Review of Financial Analysis, Elsevier, vol. 90(C).
  5. 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.
  6. 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.
  7. 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.
  8. 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.
  9. 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.
  10. 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.
  11. 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).
  12. Anjara Lalaina Jocelyn Rakotoarisoa, 2024. "Modélisations Univariées de l’Inflation Mensuelle à Madagascar : l’Atout du Modèle LSTM, un Réseau de Neurones Récurrents," Post-Print hal-04766563, HAL.
  13. 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.
  14. Himanshu Gupta & Aditya Jaiswal, 2024. "A Study on Stock Forecasting Using Deep Learning and Statistical Models," Papers 2402.06689, arXiv.org.
  15. 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.
  16. 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.
  17. 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.
  18. 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.
  19. Won Joong Kim & Gunho Jung & Sun-Yong Choi, 2020. "Forecasting CDS Term Structure Based on Nelson–Siegel Model and Machine Learning," Complexity, Hindawi, vol. 2020, pages 1-23, July.
  20. 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.
  21. 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).
  22. Michal Mec & Mikulas Zeman & Klara Cermakova, 2024. "Stock market prediction using Generative Adversarial Network (GAN) – Study case Germany stock market," International Journal of Economic Sciences, European Research Center, vol. 13(2), pages 87-103, December.
  23. 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).
  24. 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.
  25. 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).
  26. 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.
  27. 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.
  28. 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).
  29. 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.
  30. Jonas Hanetho, 2023. "Deep Policy Gradient Methods in Commodity Markets," Papers 2308.01910, arXiv.org.
  31. 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.
  32. 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.
  33. Banerjee, Ameet Kumar & Sensoy, Ahmet & Goodell, John W. & Mahapatra, Biplab, 2024. "Impact of media hype and fake news on commodity futures prices: A deep learning approach over the COVID-19 period," Finance Research Letters, Elsevier, vol. 59(C).
  34. 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.
  35. 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.
  36. 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).
  37. 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.
  38. 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.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.