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LSTM Based Time Series Forecasting of Noisy Signals

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Recent Challenges in Intelligent Information and Database Systems (ACIIDS 2024)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2145))

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Abstract

Time series analysis of signals is important to understand, track the pattern of variation of information and develop a suitable prediction model. The prediction model is used to generate, reproduce and forecast the values of the time series data without the practical experimental set up where the information can be useful for various applications. In this paper, Long Short Term Memory (LSTM) neural network based time series prediction for noisy signal is investigated using MATLAB simulations. The effect of noise on the prediction is studied by varying the level of the additive noise strength on the useful data in terms of standard deviation. From the simulation results, it has been found that LSTM can effectively forecast the time variation of the signal even in the presence of additive noise signal. The result also shows that the Root Mean Square Error (RMSE) prediction parameter is increasing with an increase in the level of the noise standard deviation.

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References

  1. Lindemann, B., Müller, T., Vietz, H., Jazdi, N., Weyrich, M.: A survey on long short-term memory networks for time series prediction. Procedia CIRP 99, 650–655 (2021). https://doi.org/10.1016/j.procir.2021.03.088

    Article  Google Scholar 

  2. De Mulder, W., Bethard, S.: Marie-Francine Moens; A survey on the application of recurrent neural networks to statistical language modeling. Comput. Speech Lang. 30(1), 61–98 (2015). https://doi.org/10.1016/j.csl.2014.09.005

    Article  Google Scholar 

  3. Zhanga, J., Wanga, P., Yanb, R., Gaoa, R.X.: Long short-term memory for machine remaining life prediction. J. Manuf. Syst. 48, 78–86 (2018). https://doi.org/10.1016/j.jmsy.2018.05.011

    Article  Google Scholar 

  4. Shah, S.R.B., Chadha, G.S., Schwung, A., Ding, S.X.: A sequence-to-sequence approach for remaining useful lifetime estimation using attention-augmented bidirectional LSTM. Intell. Syst. Appl. 10, 1–18 (2021). https://doi.org/10.1016/j.iswa.2021.200049

    Article  Google Scholar 

  5. Wang, Y., Zhao, Y., Addepalli, S.: Remaining useful life prediction using deep learning approaches: a review. Procedia Manufact. 49, 81–88 (2020). https://doi.org/10.1016/j.promfg.2020.06.015

    Article  Google Scholar 

  6. Viadinugroho, R.A.A., Rosadi, D.: Long short-term memory neural network model for time series forecasting: case study of forecasting IHSG during covid-19 outbreak. J. Phys. Conf. Ser. 1863, 1–11 (2021). https://doi.org/10.1088/1742-6596/1863/1/012016

    Article  Google Scholar 

  7. Nandakumar, R., Uttamraj, K.R., Vishal, R., Lokeswari, Y.V.: Stock price prediction using long short term memory. Int. Res. J. Eng. Technol. (IRJET) 05(03), 3342–3348 (2018)

    Google Scholar 

  8. Sudriani, Y., Ridwansyah, I., Rustini, H.A.: Long short term memory (LSTM) recurrent neural network (RNN) for discharge level prediction and forecast in Cimandiri river, Indonesia. IOP Conf. Ser. Earth Environ. Sci. 299, 1–8 (2019). https://doi.org/10.1088/1755-1315/299/1/012037

    Article  Google Scholar 

  9. Kong, Y.-L., Huang, Q., Wang, C., Chen, J., Chen, J., He, D.: Long short-term memory neural networks for online disturbance detection in satellite image time series. Remote Sens. 10, 1–13 (2018). https://doi.org/10.3390/rs10030452

    Article  Google Scholar 

  10. Zhang, K., Hong, M.: Forecasting crude oil price using LSTM neural networks. Data Sci. Finan. Econ. 2(3), 163–180 (2022). https://doi.org/10.3934/DSFE.2022008

    Article  Google Scholar 

  11. Dubey, S.R., Singh, S.K., Chaudhuri, B.B.: Activation functions in deep learning: a comprehensive survey and benchmark. Neurocomputing 503, 92–108 (2022). https://doi.org/10.1016/j.neucom.2022.06.111

    Article  Google Scholar 

  12. Kılıçarslan, S., Adem, K., Çelik, M.: An overview of the activation functions used in deep learning algorithms. J. New Results Sci. 10(3), 75–88 (2021). https://doi.org/10.54187/jnrs.1011739

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Acknowledgments

The author appreciates the financial support provided by the American University of Ras Al Khaimah (AURAK), Ras Al Khaimah, United Arab Emirates.

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Correspondence to Beza Negash Getu .

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Getu, B.N. (2024). LSTM Based Time Series Forecasting of Noisy Signals. In: Nguyen, N.T., et al. Recent Challenges in Intelligent Information and Database Systems. ACIIDS 2024. Communications in Computer and Information Science, vol 2145. Springer, Singapore. https://doi.org/10.1007/978-981-97-5934-7_12

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  • DOI: https://doi.org/10.1007/978-981-97-5934-7_12

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-97-5933-0

  • Online ISBN: 978-981-97-5934-7

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