Abstract
The variation and dependency on diverse parameters of time series data make predictions a very complicated process. Also, the effectiveness and efficiency for predicting the values of a time series are important in a variety of areas such as stock exchanges, natural language processing, and sensor networks. Artificial neural networks have been demonstrated to be valuable in such cases to predict the time series data. As recurrent neural network (RNN) is more suitable for predictions of sequential data, we are considering long short-term memory (LSTM) and gated recurrent unit (GRU) in this paper. For comparing with different deep learning frameworks, we use Keras because Keras makes an environment to run new experiments easier and faster. In this paper, we provide a comparative analysis of different deep learning frameworks on the basis of training speed and accuracy of prediction using different metrics, namely mean absolute error (MAE), root mean square error (RMSE), and time taken by different frameworks. The proposed method is expected to be a promising method in the field of time series prediction to choose the suitable deep learning frameworks for a deep learning model.
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References
A. Graves, M. Liwicki, S. Fernández, R. Bertolami, H. Bunke, J. Schmidhuber, A novel connectionist system for unconstrained handwriting recognition. IEEE Trans. Pattern Anal. Mach. Intell. 31(5), 855–868 (2008)
A. Graves, A.R. Mohamed, G. Hinton, Speech recognition with deep recurrent neural networks, in 2013 IEEE International Conference on Acoustics, Speech and Signal Processing 26 May 2013 (pp. 6645–6649). IEEE
J. Su, S. Wu, D. Xiong, Y. Lu, X. Han, B. Zhang, Variational recurrent neural machine translation, in Thirty-Second AAAI Conference on Artificial Intelligence (27 Apr 2018)
R. Weron, A. Misiorek, Forecasting spot electricity prices: a comparison of parametric and semiparametric time series models. Int. J. Forecast. 24(4), 744–763 (2008)
J. Fan, Q. Yao, Nonlinear Time Series: Nonparametric and Parametric Methods. (Springer Science and Business Media, 11 Sep 2008)
R.F. Engle, Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica: J. Econometric Soc. 987–1007 (1 Jul 1982)
Q. Zhuge, L. Xu, G. Zhang, LSTM neural network with emotional analysis for prediction of stock price. Eng. Lett. 25(2) (1 Apr 2017)
A. Lapedes, R. Farber, Nonlinear Signal Processing Using Neural Networks: Prediction and System Modelling. (1 Jun 1987)
C. de Groot, D. Würtz, Analysis of univariate time series with connectionist nets: a case study of two classical examples. Neurocomputing. 3(4), 177–192 (1991)
C.M. Kuan, T. Liu, Forecasting exchange rates using feedforward and recurrent neural networks. J. Appl. Econometrics. 10(4), 347–364 (1995)
A. Dagar, R. Bala, R.P. Singh, Financial Time Series Forecasting Using Deep Learning Network, in International Conference on Application of Computing and Communication Technologies 9 Mar 2018 (pp. 23–33). (Springer, Singapore)
F.A. Gers, D. Eck, J. Schmidhuber, Applying LSTM to time series predictable through time-window approaches, in Neural Nets WIRN Vietri-01 2002 (pp. 193–200). (Springer, London)
S. Bhanja, A. Das, Impact of Data Normalization on Deep Neural Network for Time Series Forecasting. arXiv preprint arXiv:1812.05519. (13 Dec 2018)
S. Bhanja, A. Das, Deep Neural Network for Multivariate Time Series Forecasting. International Conference on Frontiers in Computing and Systems (COMSYS-2020). (2020)
S. Bhanja, A. Das, Deep learning-based integrated stacked model for the stock market prediction. Int. J. Eng. Adv. Technol. (9), 5167–5174 (Oct 2019)
J.L. Elman, Finding structure in time. Cogn. Sci. 14(2), 179–211 (1990)
Y. LeCun, Y. Bengio, G. Hinton, Deep learning. Nature 521(7553), 436–444 (2015)
C. Olah, Understanding lstm networks. (Aug 2015)
A. Moawad, The magic of lstm neural networks. (Feb 2018)
K. Cho, B. Van Merriënboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, Y. Bengio, Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078. (3 Jun 2014)
Y. Bengio, P. Simard, P. Frasconi, Learning long-term dependencies with gradient descent is difficult. IEEE Trans. Neural Networks 5(2), 157–166 (1994)
T. Tieleman, G. Hinton, Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude. COURSERA: Neural Netw. Mach. Learn. 4(2), 26–31 (Oct 2012)
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Ali, D., Tiwari, N., Das, B., Bhanja, S., Das, A. (2021). Deep Learning Approaches to Improve Effectiveness and Efficiency for Time Series Prediction. In: Balas, V.E., Hassanien, A.E., Chakrabarti, S., Mandal, L. (eds) Proceedings of International Conference on Computational Intelligence, Data Science and Cloud Computing. Lecture Notes on Data Engineering and Communications Technologies, vol 62. Springer, Singapore. https://doi.org/10.1007/978-981-33-4968-1_20
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DOI: https://doi.org/10.1007/978-981-33-4968-1_20
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