Skip to main content
SpringerLink
Log in

A novel deep transfer learning framework with adversarial domain adaptation: application to financial time-series forecasting

  • Review
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Financial market prediction is generally regarded as one of the most challenging tasks in data mining. Recent deep learning models have achieved success in improving the accuracy of financial time-series forecasting (TSF), but as implicit complex information, and there have few available labeled data, the generalization capability of current benchmarks is poor in this field. To alleviate the restriction of overfitting caused by insufficient clean data, a novel deep transfer learning framework incorporating adversarial domain adaptation is proposed for financial TSF task, dubbed as ADA-FTSF for short, in improving reliable, accurate and competitive deep forecasting models. Concretely, we implement a typical adversarial domain adaptation architecture to transfer feature knowledge and reduce the distribution discrepancy between financial datasets. To reduce the shape difference during the pre-train process, a smoothed formulation of dynamic time warping (DTW) is also introduced tactfully in adversarial training phase to measure the shape loss. Notably, the confident selection of source domain from potential source datasets will make significant impact on forecasting performance. In our study, appropriate source dataset is selected using temporal causal discovery method via transfer entropy derived from copula entropy. The feasibility and effectiveness of the proposed framework are validated by the empirical experiments, ablation study, Diebold–Mariano test and parameter sensitivity analysis conducting on different financial datasets of three domains (financial indexes, energy futures and agricultural futures).

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Data availability

The datasets analyzed during the current study are available from the corresponding author on reasonable request.

References

  1. Janacek G (2010) Time series analysis forecasting and control. J Time Ser Anal 31(4):303–303

    Article  Google Scholar 

  2. Lee YS, Tong LI (2011) Forecasting time series using a methodology based on autoregressive integrated moving average and genetic programming. Knowl Based Syst 24(1):66–72

    Article  Google Scholar 

  3. Corberán-Vallet A, Bermúdez JD, Vercher E (2011) Forecasting correlated time series with exponential smoothing models. Int J Forecast 27(2):252–265

    Article  Google Scholar 

  4. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444

    Article  Google Scholar 

  5. Fischer T, Krauss C (2018) Deep learning with long short-term memory networks for financial market predictions. Eur J Oper Res 270(2):654–669

    Article  MathSciNet  MATH  Google Scholar 

  6. Aryal S, Nadarajah D, Rupasinghe PL et al (2020) Comparative analysis of deep learning models for multi-step prediction of financial time series. J Comput Sci 16(10):1401–1416

    Article  Google Scholar 

  7. Sezer OB, Gudelek MU, Ozbayoglu AM (2020) Financial time series forecasting with deep learning: a systematic literature review: 2005–2019. Appl Soft Comput 90(106):181

    Google Scholar 

  8. Li AW, Bastos GS (2020) Stock market forecasting using deep learning and technical analysis: a systematic review. IEEE Access 8:185,232–185,242

  9. Niu T, Wang J, Lu H et al (2020) Developing a deep learning framework with two-stage feature selection for multivariate financial time series forecasting. Expert Syst Appl 148(113):237

    Google Scholar 

  10. Tan C, Sun F, Kong T, et al (2018) A survey on deep transfer learning. In: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4–7, 2018, Proceedings, Part III 27, Springer, pp 270–279

  11. Pan SJ, Yang Q (2010) A survey on transfer learning. IEEE Trans Knowl Data Eng 22(10):1345–1359

    Article  Google Scholar 

  12. Weiss K, Khoshgoftaar TM, Wang D (2016) A survey of transfer learning. J Big Data 3(1):1–40

    Article  Google Scholar 

  13. Amaral T, Silva LM, Alexandre LA, et al (2014) Transfer learning using rotated image data to improve deep neural network performance. In: Image Analysis and Recognition: 11th International Conference, ICIAR 2014, Vilamoura, Portugal, October 22–24, 2014, Proceedings, Part I 11, Springer, pp. 290–300.

  14. Vu NT, Imseng D, Povey D, et al (2014) Multilingual deep neural network based acoustic modeling for rapid language adaptation. In: 2014 IEEE International Conference on Acoustics, speech and signal processing (ICASSP), IEEE, pp 7639–7643

  15. Ye R, Dai Q (2018) A novel transfer learning framework for time series forecasting. Knowl Based Syst 156:74–99

    Article  Google Scholar 

  16. Gu Q, Dai Q, Yu H et al (2021) Integrating multi-source transfer learning, active learning and metric learning paradigms for time series prediction. Appl Soft Comput 109(107):583

    Google Scholar 

  17. Gu Q, Dai Q (2021) A novel active multi-source transfer learning algorithm for time series forecasting. Appl Intell 51:1326–1350

    Article  Google Scholar 

  18. Ye R, Dai Q (2021) Implementing transfer learning across different datasets for time series forecasting. Pattern Recognit 109(107):617

    Google Scholar 

  19. Yang T, Yu X, Ma N et al (2021) A novel domain adaptive deep recurrent network for multivariate time series prediction. Eng Appl Artif Intell 106(104):498

    Google Scholar 

  20. Diebold FX, Mariano RS (2020) Comparing predictive accuracy. J Bus Econ Statis 20(1):134–144

    Article  MathSciNet  Google Scholar 

  21. Li G, Zhang A, Zhang Q et al (2022) Pearson correlation coefficient-based performance enhancement of broad learning system for stock price prediction. IEEE Trans Circ Syst II Express Briefs 69(5):2413–2417

    Google Scholar 

  22. Zhang Y, Yan B, Memon A (2020) A novel deep learning framework: prediction and analysis of financial time series using ceemd and lstm. Expert Syst Appl 159(113):609

    Google Scholar 

  23. Li J, Wang J (2020) Forcasting of energy futures market and synchronization based on stochastic gated recurrent unit model. Energy 213(118):78723

    Google Scholar 

  24. Lei B, Zhang B, Song Y (2021) Volatility forecasting for high-frequency financial data based on web search index and deep learning model. Mathematics 9(4):320

    Article  Google Scholar 

  25. Laptev N, Yu J, Rajagopal R (2018) Reconstruction and regression loss for time-series transfer learning. In: Proceedings of the Special Interest Group on Knowledge Discovery and Data Mining (SIGKDD) and the 4th Workshop on the Mining and Learning from Time Series (MiLeTS), London, UK

  26. Ye R, Dai Q (2022) A relationship-aligned transfer learning algorithm for time series forecasting. Inf Sci 593:17–34

    Article  Google Scholar 

  27. Nguyen TT, Yoon S (2019) A novel approach to short-term stock price movement prediction using transfer learning. Appl Sci 9(22):4745

    Article  Google Scholar 

  28. He QQ, Pang PCI, Si YW (2019) Transfer learning for financial time series forecasting. In: PRICAI 2019: Trends in Artificial Intelligence: 16th Pacific Rim International Conference on Artificial Intelligence, Cuvu, Yanuca Island, Fiji, August 26–30, 2019, Proceedings, Part II 16, Springer, pp 24–36

  29. Runge J, Bathiany S, Bollt E et al (2019) Inferring causation from time series in earth system sciences. Nat Commun 10(1):2553

    Article  Google Scholar 

  30. Reid AT, Headley DB, Mill RD et al (2019) Advancing functional connectivity research from association to causation. Nat Neurosci 22(11):1751–1760

    Article  Google Scholar 

  31. Sachs K, Perez O, Pe’er D, et al (2005) Causal protein-signaling networks derived from multiparameter single-cell data. Science 308(5721):523–529

    Article  Google Scholar 

  32. Qiu H, Liu Y, Subrahmanya NA, et al (2012) Granger causality for time-series anomaly detection. In: 2012 IEEE 12th International Conference on Data Mining (ICDM), IEEE, pp 1074–1079

  33. Vuković M, Thalmann S (2022) Causal discovery in manufacturing: a structured literature review. J Manuf Mater Process 6(1):10

    Google Scholar 

  34. Arabzadeh N, Fani H, Zarrinkalam F, et al (2018) Causal dependencies for future interest prediction on twitter. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management, pp 1511–1514

  35. Granger CW (1980) Testing for causality: a personal viewpoint. J Econ Dyn Control 2:329–352

    Article  MathSciNet  Google Scholar 

  36. Schreiber T (2000) Measuring information transfer. Phys Rev Lett 85(2):461–464

    Article  Google Scholar 

  37. Ma J, Sun Z (2011) Mutual information is copula entropy. Tsinghua Sci Technol 16(1):51–54

    Article  MATH  Google Scholar 

  38. Ma J (2019) Estimating transfer entropy via copula entropy. arXiv preprint http://arxiv.org/abs/1910.04375v3

  39. Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780

    Article  Google Scholar 

  40. Chung J, Gulcehre C, Cho K, et al (2014) Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint https://arxiv.org/abs/1412.3555

  41. Bai S, Kolter JZ, Koltun V (2018) An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv preprint https://arxiv.org/abs/1803.01271

  42. Ganin Y, Ustinova E, Ajakan H et al (2016) Domain-adversarial training of neural networks. J Mach Learn Res 17(1):2096–2030

    MathSciNet  MATH  Google Scholar 

  43. Smola A, Gretton A, Song L, et al (2007) A hilbert space embedding for distributions. In: Algorithmic Learning Theory: 18th International Conference, ALT 2007, Sendai, Japan, October 1–4, 2007. Proceedings 18, Springer, pp 13–31

  44. Sakoe H, Chiba S (1978) Dynamic programming algorithm optimization for spoken word recognition. IEEE Trans Acoust Speech Signal Process 26(1):43–49

    Article  MATH  Google Scholar 

  45. Cuturi M, Blondel M (2017) Soft-dtw: a differentiable loss function for time-series. In: Proceedings of the 34th International Conference on Machine Learning (ICML) vol 70 pp 894–903

  46. Le Guen V, Thome N (2022) Deep time series forecasting with shape and temporal criteria. IEEE Trans Pattern Anal Mach Intell 45(1):342–355

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China (71971089 and 72001083), the Project of the Humanities and Social Sciences Program of the Chinese Ministry of Education (20YJC740067) and the Natural Science Foundation of Guangdong Province (No. 2022A1515011612).

Author information

Authors and Affiliations

  1. College of Mathematics and Informatics, South China Agricultural University, Guangzhou, 510642, China

    Dabin Zhang, Ruibin Lin, Tingting Wei, Liwen Ling & Junjie Huang

Authors
  1. Dabin Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ruibin Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tingting Wei
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Liwen Ling
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Junjie Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ruibin Lin.

Ethics declarations

Conflict of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, D., Lin, R., Wei, T. et al. A novel deep transfer learning framework with adversarial domain adaptation: application to financial time-series forecasting. Neural Comput & Applic 35, 24037–24054 (2023). https://doi.org/10.1007/s00521-023-09047-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-023-09047-1

Keywords

Search

Navigation