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Smart city article details

Title	Time Series Data Analysis Using Deep Learning Methods For Smart Cities Monitoring
ID_Doc	57409
Authors	Ciaburro G.
Year	2022
Published	Studies in Computational Intelligence, 994
DOI	http://dx.doi.org/10.1007/978-3-030-87954-9_4
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. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Author Keywords	Deep learning; Smart Cities; Time Series analysis; Time Series forecasting

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