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Time Series Data Analysis Using Deep Learning Methods for Smart Cities Monitoring

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Big Data Intelligence for Smart Applications

Part of the book series: Studies in Computational Intelligence ((SCI,volume 994))

Abstract

A time series is a sequence of empirical data ordered as a function of time. Time series analysis models exploit forecasting techniques based solely on the history of the variable of interest. They work by capturing patterns in historical data and extrapolating them into the future. The Times Series features recurring structures that can be captured through careful and precise analysis of its performance. Machine Learning-based methods are able to identify these recurring structures fully automatically. In this chapter we have faced the problem of the elaboration of forecasting models based on Deep Learning algorithms for data with time series characteristics. First, we introduced the Time Series, and we analyzed the most popular forecast models based on the traditional methodologies of classical Statistics. Next, we introduced Deep Learning-based methodologies that are inspired by the structure and function of the brain and which have proven effective in capturing the recurring characteristics of time series. In this context, we developed a model based on Recurrent Neural Networks for the prediction of equivalent noise levels produced by a road infrastructure. The model based on the LSTM was able to memorize the recurring structures present in the trend of the noise values ​​and in the forecast, it preserved the daily and weekly trend characteristics already verified through a visual analysis.

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Author information

Authors and Affiliations

  1. Department of Architecture and Industrial Design, Università degli Studi della Campania Luigi Vanvitelli, 81031, Borgo San Lorenzo, Aversa, CE, Italy

    Giuseppe Ciaburro

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  1. Giuseppe Ciaburro
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Correspondence to Giuseppe Ciaburro .

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Editors and Affiliations

  1. Chouaib Doukkali University, El Jadida, Morocco

    Youssef Baddi

  2. National School of Applied Sciences, Ibn Tofail University, Kenitra, Morocco

    Youssef Gahi

  3. Sultan Moulay Slimane University, Settat, Morocco

    Yassine Maleh

  4. Charles Darwin University, Canberra ACT, Australia

    Mamoun Alazab

  5. Texas A&M University, San Antonio, TX, USA

    Loai Tawalbeh

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Ciaburro, G. (2022). Time Series Data Analysis Using Deep Learning Methods for Smart Cities Monitoring. In: Baddi, Y., Gahi, Y., Maleh, Y., Alazab, M., Tawalbeh, L. (eds) Big Data Intelligence for Smart Applications. Studies in Computational Intelligence, vol 994. Springer, Cham. https://doi.org/10.1007/978-3-030-87954-9_4

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