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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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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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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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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