• P-ISSN 0974-6846 E-ISSN 0974-5645

Indian Journal of Science and Technology


Indian Journal of Science and Technology

Year: 2021, Volume: 14, Issue: 31, Pages: 2557-2566

Original Article

An Application of AdaBoost-GRU Ensemble Model to Economic Time Series Prediction

Received Date:28 June 2021, Accepted Date:17 August 2021, Published Date:22 September 2021


Objectives: Given the importance of accurate prediction of financial time series data and their benefits in the real-life, AdaBoost-GRU ensemble learning is proposed in which it’s forecasting accuracy is to be compared with AdaBoost-LSTM, single Long Short Term Memory (LSTM), and single Gated Recurrent Unit (GRU). Methods: The data for Korea Composite Stock Price Index (KOSPI) obtained from Naver Finance from January 2000 to April 2020, the Oil Price data for the entire Gyeongnam region among domestic oil price data obtained from Korea Petroleum Corporation (Opinet) and USD Exchange data provided by Naver Financial from April 2004 to May 2020 were employed. The analyses were made using mean absolute error (MAE), mean squared error (MSE) and root mean squared error (RMSE) as the performance metric. Findings: Empirical results show that the proposed method outperforms all other models that serve as benchmarked models, in all three kinds of data used in this research. This also shows that ensemble models have better performance than the single models as both AdaBoost-GRU and AdaBoost-LSTM outperform their respective single GRU and single LSTM. Novelty/Applications: This empirical study suggests that the AdaBoost-GRU ensemble-learning model is a highly promising approach for forecasting these kinds of data. However, another ensemble model that can combine AdaBoost with other single models such as ConvD1 can be developed and applied.

Keywords: Oil Price; Exchange Rate; Stock Price Index; Time Series Forecasting; AdaBoost Algorithm; Gated Recurrent Unit


  1. Sun S, Wei Y, Wang S. Adaboost-lstm ensemble learning for financial time series forecastingInInternational Conference on Computational Science2018;p. 590597. Available from: https://www.iccs-meeting.org/archive/iccs2018/papers/108620563.pdf
  2. Wang JH, Leu JY. Stock market trend prediction using ARIMA-based neural networksInProceedings of International Conference on Neural Networks (ICNN'96)1996;4:21602165. doi: 10.1109/ICNN.1996.549236
  3. Areekul P, Senjyu T, Toyama H, Yona A. Notice of violation of ieee publication principles: A hybrid arima and neural network model for short-term price forecasting in deregulated marketIEEE Transactions on Power Systems2009;25(1):524530. doi: 10.1109/TPWRS.2009.2036488
  4. Chandar S, Kumar M, Sumathi SN, Sivanandam. Prediction of stock market price using hybrid of wavelet transform and artificial neural network. Indian journal of Science and Technology2016;9:15. Available from: DOI:10.17485/ijst/2016/v9i8/87905
  5. Tsantekidis A, Passalis N, Tefas A, Kanniainen J. Forecasting stock prices from the limit order book using convolutional neural networksIn2017 IEEE 19th Conference on Business Informatics (CBI)2017;1:712. doi: 10.1109/CBI.2017.23
  6. Ding X, Zhang Y, Liu T, Duan J. Deep learning for event-driven stock predictionInTwenty-fourth international joint conference on artificial intelligence2015. Available from: http://www.wins.or.kr/DataPool/Board/4xxxx/455xx/45587/329.pdf
  7. Cho K, Merriënboer BV, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, et al. Learning phrase representations using RNN encoder-decoder for statistical machine translation. 2014. Available from: https://arxiv.org/pdf/1406.1078.pdf
  8. Lopez-MartinManuel A, Sanchez-Esguevillas L, Hernandez-Callejo JI, Arribas B, Carro. Novel Data-Driven Models Applied to Short-Term Electric Load ForecastingApplied Sciences2021;11(12):5708. doi: 10.3390/app11125708
  9. Wang G, Tao T, Ma J, Li H, Fu H, Chu Y. An improved ensemble learning method for exchange rate forecasting based on complementary effect of shallow and deep featuresExpert Systems with Applications2021;184(115569). doi: 10.1016/j.eswa.2021.115569
  10. Lopez-MartinManuel B, Carro A, Sanchez-Esguevillas. IoT type-of-traffic forecasting method based on gradient boosting neural networksFuture Generation Computer Systems2020;105:331345. doi: 10.1016/j.future.2019.12.013
  11. Silva RGD, Ribeiro MH, Moreno SR, Mariani VC, Coelho LS. A novel decomposition-ensemble learning framework for multi-step ahead wind energy forecastingEnergy2021;216(119174). doi: 10.1016/j.energy.2020.119174
  12. Pinto T, Praça I, Vale Z, Silva J. Ensemble learning for electricity consumption forecasting in office buildingsNeurocomputing2021;423:747755. doi: 10.1016/j.neucom.2020.02.124
  13. Freund Y, Schapire RE. A decision-theoretic generalization of on-line learning and an application to boostingJournal of computer and system sciences1997;55:119139. Available from: https://www.sciencedirect.com/science/article/pii/S002200009791504X
  14. Schapire RE. Explaining adaboost. InEmpirical inference. (pp. 37-52) Berlin, Heidelberg. Springer. 2013.
  15. Freund Y, Schapire R, Abe N. A short introduction to boostingJournal-Japanese Society For Artificial Intelligence1999;14:1612. Available from: http://www.yorku.ca/gisweb/eats4400/boost.pdf
  16. Zhang A, Lipton ZC, Li M, Smola AJ. Dive into deep learning. Unpublished Draft. 2019. Available from: https://d2l.ai/d2l-en.pdf
  17. Diao E, Ding J, Tarokh V. Restricted recurrent neural networksIn2019 IEEE International Conference on Big Data (Big Data)2009;p. 5663. Available from: https://arxiv.org/pdf/1908.07724
  18. Elshendy M, Colladon AF, Battistoni E, Gloor PA. Using four different online media sources to forecast the crude oil priceJournal of Information Science2018;44(3):408421. Available from: https://doi.org/10.1177/0165551517698298
  19. Ahmed NK, Amir F, Atiya NE, Gayar H, El-Shishiny. An empirical comparison of machine learning models for time series forecastingEconometric Reviews2010;29(5-6):594621. Available from: https://www.tandfonline.com/doi/abs/10.1080/07474938.2010.481556
  20. Mcadam P, Mcnelis P. Forecasting inflation with thick models and neural networksEconomic Modelling2005;22:848867. Available from: https://www.sciencedirect.com/science/article/pii/S026499930500043X
  21. Zhang G, Patuwo BE, Hu MY. Forecasting with artificial neural networks: The state of the art. International journal of forecasting1998;14(1):3562. Available from: https://www.sciencedirect.com/science/article/pii/S0169207097000447


© 2021 Busari et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)


Subscribe now for latest articles and news.