Movatterモバイル変換

[0]ホーム
URL:

Skip to main content

Advertisement

Springer Nature Link
Log in

A novel validation framework to enhance deep learning models in time-series forecasting

  • S.I. : Emerging applications of Deep Learning and Spiking ANN
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Time-series analysis and forecasting is generally considered as one of the most challenging problems in data mining. During the last decade, powerful deep learning methodologies have been efficiently applied for time-series forecasting; however, they cannot guarantee the development of reliable prediction models. In this work, we introduce a novel framework for supporting deep learning in enhancing accurate, efficient and reliable time-series models. The major novelty of our proposed methodology is that it ensures a time-series to be “suitable” for fitting a deep learning model by performing a series of transformations in order to satisfy the stationarity property. The enforcement of stationarity is performed by the application of Augmented Dickey–Fuller test and transformations based on first differences or returns, without the loss of any embedded information. The reliability of the deep learning model’s predictions is guaranteed by rejecting the hypothesis of autocorrelation in the model’s errors, which is demonstrated by autocorrelation function plots and Ljung–BoxQ test. Our numerical experiments were performed utilizing time-series from three real-world application domains (financial market, energy sector, cryptocurrency area), which incorporate most of global research interest. The performance of all forecasting models was compared on both problems of forecasting time-series price (regression) and time-series directional movements (classification). Additionally, the reliability of the models’ forecasts was evaluated by examining the existence of autocorrelation in the errors. Our numerical experiments indicate that our proposed methodology considerably improves the forecasting performance of a deep learning model, in terms of efficiency, accuracy and reliability.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

ArticleOpen access25 November 2024

Explore related subjects

Discover the latest articles and news from researchers in related subjects, suggested using machine learning.

Notes

  1. Non-stationary time-series which can be transformed in this way are called series integrated of orderd. Usually, the order of integration is eitherI(0) orI(1); it’s extremely rare to see values ford that are 2 or more in real-world applications[7]. Additionally, all series in this research areI(1).

References

  1. Ahmed NK, Atiya AF, Gayar NE, El-Shishiny H (2010) An empirical comparison of machine learning models for time series forecasting. Econom Rev 29(5–6):594–621

    Article MathSciNet  Google Scholar 

  2. Bengio Y, Courville A, Vincent P (2013) Representation learning: a review and new perspectives. IEEE Trans Pattern Anal Mach Intell 35(8):1798–1828

    Article  Google Scholar 

  3. Bontempi G, Taieb SB, Le Borgne Y (2012) Machine learning strategies for time series forecasting. In: European business intelligence summer school. Springer, Berlin, pp 62–77

  4. Bougoudis I, Demertzis K, Iliadis L (2016) HISYCOL a hybrid computational intelligence system for combined machine learning: the case of air pollution modeling in Athens. Neural Comput Appl 27(5):1191–1206

    Article  Google Scholar 

  5. Box GEP, Jenkins GM, Reinsel GC, Ljung GM (2015) Time series analysis: forecasting and control. Wiley, London

    MATH  Google Scholar 

  6. Brockwell PJ, Davis RA (2016) Introduction to time series and forecasting. Springer, Berlin

    Book MATH  Google Scholar 

  7. Burke S, Hunter J (2005) Modelling non-stationary economic time series: a multivariate approach. Springer, Berlin

    Book  Google Scholar 

  8. Cen Z, Wang J (2019) Crude oil price prediction model with long short term memory deep learning based on prior knowledge data transfer. Energy 169:160–171

    Article  Google Scholar 

  9. Chambon S, Galtier MN, Arnal PJ, Wainrib G, Gramfort A (2018) A deep learning architecture for temporal sleep stage classification using multivariate and multimodal time series. IEEE Trans Neural Syst Rehabil Eng 26(4):758–769

    Article  Google Scholar 

  10. Donate JP, Li X, Sánchez GG, de Miguel AS (2013) Time series forecasting by evolving artificial neural networks with genetic algorithms, differential evolution and estimation of distribution algorithm. Neural Comput Appl 22(1):11–20

    Article  Google Scholar 

  11. Fawaz HI, Forestier G, Weber J, Idoumghar L, Muller P (2019) Deep learning for time series classification: a review. Data Min Knowl Disc 33(4):917–963

    Article MathSciNet  Google Scholar 

  12. 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 

  13. Fu T (2011) A review on time series data mining. Eng Appl Artif Intell 24(1):164–181

    Article  Google Scholar 

  14. Gocheva-Ilieva SG, Voynikova DS, Stoimenova MP, Ivanov AV, Iliev IP (2019) Regression trees modeling of time series for air pollution analysis and forecasting. Neural Comput Appl 31(12):9023–9039

    Article  Google Scholar 

  15. Gulli A, Pal S (2017) Deep learning with Keras. Packt Publishing Ltd

  16. Ji S, Kim J, Im H (2019) A comparative study of Bitcoin price prediction using deep learning. Mathematics 7(10):898

    Article  Google Scholar 

  17. Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: 2015 International conference on learning representations

  18. Liu S, Zhang C, Ma J (2017) CNN-LSTM neural network model for quantitative strategy analysis in stock markets. In: International conference on neural information processing. Springer, Berlin, pp 198–206

  19. Livieris IE, Pintelas E, Kiriakidou N, Stavroyiannis S (2020) An advanced deep learning model for short-term forecasting U.S. natural gas price and movement. In: 16th International conference on artificial intelligence applications and innovations

  20. Livieris IE, Pintelas E, Pintelas P (2020) A CNN–LSTM model for gold price time series forecasting. In: Neural computing and applications

  21. Nakano M, Takahashi A, Takahashi S (2018) Bitcoin technical trading with artificial neural network. Physica A 510:587–609

    Article  Google Scholar 

  22. Osborne J (2010) Improving your data transformations: applying the Box-Cox transformation. Pract Assess Res Eval 15(1):12

    Google Scholar 

  23. Pal A, Prakash PKS (2017) Practical time series analysis: master time series data processing, visualization, and modeling using python. Packt Publishing Ltd

  24. Pintelas E, Livieris IE, Stavroyiannis S, Kotsilieris T, Pintelas P (2020) Fundamental research questions and proposals on predicting cryptocurrency prices using DNNs. Technical Report TR20-01, University of Patras,https://nemertes.lis.upatras.gr/jspui/bitstream/10889/13296/1/TR01-20.pdf

  25. Pintelas E, Livieris IE, Stavroyiannis S, Kotsilieris T, Pintelas P (2020) Investigating the problem of cryptocurrency price prediction: a deep learning approach. In: 16th International conference on artificial intelligence applications and innovations

  26. Shaman P (2010) Generalized Levinson–Durbin sequences, binomial coefficients and autoregressive estimation. J Multivar Anal 101(5):1263–1273

    Article MathSciNet MATH  Google Scholar 

  27. Stavroyiannis S (2019) Can Bitcoin diversify significantly a portfolio? Int J Econ Bus Res 18(4):399–411

    Article  Google Scholar 

  28. Tanaka K (2017) Time series analysis: nonstationary and noninvertible distribution theory, vol 4. Wiley, London

    Book MATH  Google Scholar 

  29. Urtnasan E, Park J, Lee K (2018) Automatic detection of sleep-disordered breathing events using recurrent neural networks from an electrocardiogram signal. In: Neural computing and applications, pp 1–10

  30. Weigend AS (2018) Time series prediction: forecasting the future and understanding the past. Routledge, London

    Book  Google Scholar 

  31. Xingjian SHI, Chen Z, Wang H, Yeung D, Wong W, Woo W (2015) Convolutional LSTM network: a machine learning approach for precipitation nowcasting. In: Advances in neural information processing systems, pp 802–810

  32. Zhao Y, Li J, Yu L (2017) A deep learning ensemble approach for crude oil price forecasting. Energy Econ 66:9–16

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Mathematics, University of Patras, 265-00, Patras, Greece

    Ioannis E. Livieris, Emmanuel Pintelas & Panagiotis Pintelas

  2. Department of Accounting and Finance, University of the Peloponnese, 241-00, Antikalamos, Greece

    Stavros Stavroyiannis

Authors
  1. Ioannis E. Livieris
  2. Stavros Stavroyiannis
  3. Emmanuel Pintelas
  4. Panagiotis Pintelas

Corresponding author

Correspondence toIoannis E. Livieris.

Ethics declarations

Conflicts of interest

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Additional information

Publisher's Note

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

Rights and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Livieris, I.E., Stavroyiannis, S., Pintelas, E.et al. A novel validation framework to enhance deep learning models in time-series forecasting.Neural Comput & Applic32, 17149–17167 (2020). https://doi.org/10.1007/s00521-020-05169-y

Download citation

Keywords

Profiles

  1. Ioannis E. LivierisView author profile

Access this article

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

Instant access to the full article PDF.

Advertisement

[8]ページ先頭
©2009-2025 Movatter.jp