Abstract
This paper presents a comprehensive study on the application of machine learning techniques in the prediction of respiratory rate via time-series-based statistical and machine learning methods using several physiological signals. Two different models, ARIMA and LSTM, were developed. The LSTM model showed a stronger capacity for learning and capturing complicated patterns in the data compared to the ARIMA model. The findings imply that LSTM models, by incorporating many variables, have the ability to provide predictions that are more accurate, particularly in situations where respiratory rate values vary significantly. © 2024 The Authors. Published by ELSEVIER B.V.

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Digital Marketing sets a sequence of strategies responsible for maximizing the interaction between companies and their target audience. One of them, known as Customer Success, establishes long-term techniques capable of projecting the sustainable value of a given customer to a company, monitoring the indexers that translate its activities. Therefore, this paper intends to address the need to develop an innovative tool that allows the creation of a temporal knowledge base composed of the behavioral evolution of customers. The CRISP-DM model benefits the processing and modeling of data capable of generating knowledge through the application and combination of the results obtained by machine learning algorithms specialized in time series. Time Series K-Means allows the clustering and differentiation of consumers characterized by their similar habits. Through the formulation of profiles, it is possible to apply forecasting methods that predict the following trends. The proposed solution provides the understanding of time series that profile the flow of customer activity and the use of the evidenced dynamics for the future prediction of these behaviors. © MIR Labs, www.mirlabs.net/ijcisim/index.html

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