@inproceedings{255cb18eaa92422d888b1864764d96c0,
title = "Prevendo o Futuro: Uma Plataforma Web para Modela{\c c}{\~a}o de S{\'e}ries Temporais",
abstract = "This article describes a WEB platform for forecasting time series using the ARIMA and SARIMA models. These models are widely used to forecast time series through manual or automatic processes. The platform is designed to analyze meteorological data in time series. It integrates open-source languages and technologies such as Python and the Django framework for data visualization and decision support. As a case study, meteorological data provided by a center that operates in the area of irrigation, were used. Despite the work being in evaluation, the existing preliminary results point to high interest on the part of the members and clients of the center.",
keywords = "ARIMA, decision support, forecasting models, SARIMA, Time Series",
author = "Vidal, {Adriano Leal} and Brito, {Isabel Sofia} and Barros, {Jo{\~a}o Paulo} and Luis Domingues",
note = "Publisher Copyright: {\textcopyright} 2023 ITMA.; 18th Iberian Conference on Information Systems and Technologies, CISTI 2023 ; Conference date: 20-06-2023 Through 23-06-2023",
year = "2023",
doi = "10.23919/CISTI58278.2023.10211327",
language = "Portuguese",
isbn = "979-8-3503-0527-2",
series = "Iberian Conference on Information Systems and Technologies (CISTI)",
publisher = "IEEE Computer Society Press",
booktitle = "2023 18th Iberian Conference on Information Systems and Technologies (CISTI)",
}