Version 1
: Received: 2 November 2023 / Approved: 3 November 2023 / Online: 3 November 2023 (12:38:10 CET)
How to cite:
Kniess, J.; Oliveira, S. S. D. Data Prediction in Datasets of Internet of Things with Recurrent Neural Networks. Preprints2023, 2023110247. https://doi.org/10.20944/preprints202311.0247.v1
Kniess, J.; Oliveira, S. S. D. Data Prediction in Datasets of Internet of Things with Recurrent Neural Networks. Preprints 2023, 2023110247. https://doi.org/10.20944/preprints202311.0247.v1
Kniess, J.; Oliveira, S. S. D. Data Prediction in Datasets of Internet of Things with Recurrent Neural Networks. Preprints2023, 2023110247. https://doi.org/10.20944/preprints202311.0247.v1
APA Style
Kniess, J., & Oliveira, S. S. D. (2023). Data Prediction in Datasets of Internet of Things with Recurrent Neural Networks. Preprints. https://doi.org/10.20944/preprints202311.0247.v1
Chicago/Turabian Style
Kniess, J. and Samuel Silva de Oliveira. 2023 "Data Prediction in Datasets of Internet of Things with Recurrent Neural Networks" Preprints. https://doi.org/10.20944/preprints202311.0247.v1
Abstract
The emergence of the Internet of Things (IoT) has led to the deployment of various types of sensors in many application fields, including environment monitoring, smart cities, health, industries, and others. The increasing number of connected devices has led to the creation of massive quantities of data that need to be analyzed. Typically, this data is ordered by time, as a time series. In this context, this paper presents a time series prediction model based on Recurrent Neural Networks in order to predict one step ahead. Result obtained through five Internet of Things monitoring datasets, showed that the Recurrent Neural Network obtained better performance that the prediction methods, ARIMA and SVM.
Keywords
Dataset; Recurrent Neural Networks; Internet of Things; Time Series.
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.