Skip to main content
SpringerLink
Log in

Three stage fusion for effective time series forecasting using Bi-LSTM-ARIMA and improved DE-ABC algorithm

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Fusion is a state-of-the-art technique to observe the behavioral pattern from time series data. Fusion models efficiently and effectively interpret both linear and nonlinear patterns that are the constraints of an individual model due to feature limitations. In this paper, a three-stage fusion model is proposed to handle time series data and improve stock market forecasting accuracy. In the first phase of fusion, stock market inputs that are constituted with historical data and market sentiments of the targeted stock are pooled along with established technical indicators of the stock market. Market sentiments are examined through sentiment polarity index using big data platform Hadoop. In the second phase, Auto Regressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) are combined to observe linear and nonlinear features of the final stock dataset. In the third phase, an improved Artificial Bee Colony (ABC) algorithm using differential evolution (DE) is examined for the hyperparameter selection of the proposed DE-ABC-Bi-LSTM-ARIMA model for the stock market prediction. In this paper, experiments are performed on established and diversified reported historical datasets Dow Jones Industrial Average index, Nikkei 225 (N225) index, S&P 500 index and NASDAQ GS index. The proposed fusion model DE-ABC-Bi-LSTM-ARIMA outperformed the benchmark models used in this paper.

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
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Bustos O, Pomares-Quimbaya A (2020) Stock market movement forecast: a systematic review. Expert Syst Appl 156:113464

    Article  Google Scholar 

  2. Sezer OB, Gudelek MU, Ozbayoglu AM (2020) Financial time series forecasting with deep learning: a systematic literature review: 2005–2019. Appl Soft Comput 90:106181

    Article  Google Scholar 

  3. Thakkar A, Chaudhari K (2021) Fusion in stock market prediction: a decade survey on the necessity, recent developments, and potential future directions. Inf Fusion 65:95–107

    Article  Google Scholar 

  4. Jin Z, Yang Y, Liu Y (2019) Stock closing price prediction based on sentiment analysis and LSTM. Neural Comput Appl 66:1–17

    Google Scholar 

  5. Li X, Wu P, Wang W (2020) Incorporating stock prices and news sentiments for stock market prediction: a case of Hong Kong. Inf Process Manag 57(5):102212

    Article  Google Scholar 

  6. Kumar R, Kumar P, Kumar Y (2021) Analysis of financial time series forecasting using deep learning model. In: 2021 11th International conference on cloud computing, data science & engineering (confluence). IEEE, pp 877–881

  7. Kumar R, Kumar P, Kumar Y (2021) Integrating big data driven sentiments polarity and ABC-optimized LSTM for time series forecasting. Multimed Tools Appl 66:1–20

    Article  Google Scholar 

  8. Dash R, Dash PK (2016) A hybrid stock trading framework integrating technical analysis with machine learning techniques. J Finance Data Sci 2(1):42–57

    Article  Google Scholar 

  9. Khashei M, Bijari M (2010) An artificial neural network (p, d, q) model for time series forecasting. Expert Syst Appl 37(1):479–489

    Article  Google Scholar 

  10. Domingos SDO, de Oliveira JF, de Mattos Neto PS (2019) An intelligent hybridization of ARIMA with machine learning models for time series forecasting. Knowl Based Syst 175:72–86

    Article  Google Scholar 

  11. Yasin H, Caraka RE, Hoyyi A (2020) Stock price modeling using localized multiple kernel learning support vector machine. ICIC Express Lett Part B Appl Int J Res Surv 11(4):333–339

    Google Scholar 

  12. Kumar R, Kumar P, Kumar Y (2021) Two phase hybridization using deep learning and evolutionary algorithms for stock market forecasting. Int J Grid Util Comput 12(5–6):573–589

    Article  Google Scholar 

  13. Rikukawa S, Mori H, Harada T (2020) Recurrent neural network based stock price prediction using multiple stock brands. Int J Innov Comput Inf Control 16(3):1093–1099

    Google Scholar 

  14. Kumar R, Kumar P, Kumar Y (2021) Multi-step time series analysis and forecasting strategy using ARIMA and evolutionary algorithms. Int J Inf Technol 66:1–15

    Google Scholar 

  15. Zhou K, Wang WY, Hu T, Wu CH (2020) Comparison of time series forecasting based on statistical ARIMA model and LSTM with attention mechanism. In: Journal of physics: conference series, (vol 1631, No. 1). IOP Publishing, p 012141

  16. Xiang W, Ma S, An M (2014) Habcde: a hybrid evolutionary algorithm based on artificial bee colony algorithm and differential evolution. Appl Math Comput 238:370–386

    MathSciNet  MATH  Google Scholar 

  17. Zorarpacı E, Özel SA (2016) A hybrid approach of differential evolution and artificial bee colony for feature selection. Expert Syst Appl 62:91–103

    Article  Google Scholar 

  18. Jadon SS, Tiwari R, Sharma H, Bansal JC (2017) Hybrid artificial bee colony algorithm with differential evolution. Appl Soft Comput 58:11–24

    Article  Google Scholar 

  19. Göçken M, Özçalıcı M, Boru A, Dosdoğru AT (2016) Integrating metaheuristics and artificial neural networks for improved stock price prediction. Expert Syst Appl 44:320–331

    Article  Google Scholar 

  20. Dwivedi RK, Aggarwal M, Keshari SK, Kumar A (2019) Sentiment analysis and feature extraction using rule-based model (RBM). In: International conference on innovative computing and communications. Springer, Singapore, pp 57–63

  21. Ruan Y, Durresi A, Alfantoukh L (2018) Using Twitter trust network for stock market analysis. Knowl Based Syst 145:207–218

    Article  Google Scholar 

  22. Rodrigues AP, Chiplunkar NN (2018) Real-time Twitter data analysis using Hadoop ecosystem. Cogent Eng 5(1):1534519

    Article  Google Scholar 

  23. Yang R, Yu L, Zhao Y, Yu H, Xu G, Wu Y, Liu Z (2020) Big data analytics for financial Market volatility forecast based on support vector machine. Int J Inf Manag 50:452–462

    Article  Google Scholar 

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

    Article  Google Scholar 

  25. Schuster M, Paliwal KK (1997) Bidirectional recurrent neural networks. IEEE Trans Signal Process 45(11):2673–2681

    Article  Google Scholar 

  26. Althelaya KA, El-Alfy ESM, Mohammed S (2018) Evaluation of bidirectional LSTM for short-and long-term stock market prediction. In: 2018 9th international conference on information and communication systems (ICICS). IEEE, pp 151–156

  27. Jia M, Huang J, Pang L, Zhao Q (2019) Analysis and research on stock price of LSTM and bidirectional LSTM neural network. In: 3rd International conference on computer engineering, information science & application technology (ICCIA 2019). Atlantis Press, pp 467–473

  28. Althelaya KA, El-Alfy ESM, Mohammed S (2018) Stock market forecast using multivariate analysis with bidirectional and stacked (LSTM, GRU). In: 2018 21st Saudi Computer Society national computer conference (NCC). IEEE, pp 1–7

  29. Box GE, Jenkins GM, Reinsel GC, Ljung GM (2015) Time series analysis: forecasting and control. Wiley

    MATH  Google Scholar 

  30. Khashei M, Hajirahimi Z (2018) A comparative study of series arima/mlp hybrid models for stock price forecasting. Commun Stat Simul Comput. https://doi.org/10.1080/03610918.2018.1458138

  31. Karaboga D, Gorkemli B, Ozturk C, Karaboga N (2014) A comprehensive survey: artificial bee colony (ABC) algorithm and applications. Artif Intell Rev 42(1):21–57

    Article  Google Scholar 

  32. Storn R, Price K (1997) Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341–359

    Article  MathSciNet  Google Scholar 

  33. Qin AK, Huang VL, Suganthan PN (2008) Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Trans Evol Comput 13(2):398–417

    Article  Google Scholar 

  34. Kumar R, Kumar P, Kumar Y (2020) Time series data prediction using IoT and machine learning technique. Procedia Comput Sci 167:373–381

    Article  Google Scholar 

  35. 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  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Jaypee University of Information Technology, Waknaghat, HP, India

    Raghavendra Kumar, Pardeep Kumar & Yugal Kumar

  2. AdGlobal360 India Private Limited, Gurugram, India

    Raghavendra Kumar

Authors
  1. Raghavendra Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pardeep Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yugal Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Raghavendra Kumar.

Ethics declarations

Conflict of interest

The authors declare no potential conflicts of commercial or associative interest with respect to the research, authorship and 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

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kumar, R., Kumar, P. & Kumar, Y. Three stage fusion for effective time series forecasting using Bi-LSTM-ARIMA and improved DE-ABC algorithm. Neural Comput & Applic 34, 18421–18437 (2022). https://doi.org/10.1007/s00521-022-07431-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-022-07431-x

Keywords

Search

Navigation