Applications of the ARIMA model for time series data analysis

被引：2

作者：

Bandura, Elaine ^{[1
]}

Metinoski Bueno, Janaina Cosmedamiana ^{[2
]}

Jadoski, Guilherme Stasiak ^{[3
]}

Ribeiro Junior, Gilmar Freitas ^{[4
]}

机构：

[1] Univ Estadual Ctr Oeste UNICTR, Curso Matemat Aplicada & Computac, Guarapuava, PR, Brazil

[2] Univ Estadual Ctr Oeste UNICTR, Programa Postgrad Agron, Guarapuava, PR, Brazil

[3] Univ Tecnol Fed Parana UTFPR, Curso Engn Mecan, Campus Pato Branco, Pato Branco, PR, Brazil

[4] Univ Tecnol Fed Parana UTFPR, Curso Tecnol Sistemas Internet, Campus Guarapuava, Guarapuava, PR, Brazil

来源：

APPLIED RESEARCH & AGROTECHNOLOGY | 2019年 / 12卷 / 03期

关键词：

time series; trend assessment; seasonality;

D O I：

10.5935/PAeT.V12.N3.15

中图分类号：

S3 [农学（农艺学）];

学科分类号：

0901 ;

摘要：

The application of statistical methods, can increase the efficiency of processes. Being the forecast a prognosis of future events, used for planning and decision purposes, increasing probabilities or even ensuring success from its applicability. A time series is a set of observations properly ordered in time, presenting the crucial dependence between the observations. How much ist he longer this series, the better the chances of a satisfactory fit of the mathematical modeling. In studies considering the availability of data in time series, ARIMA models can be used to describe, interpret and understand behaviors and trends, reducing the imprecision of predictions. It is evident the expressive range of applications of the ARIMA model and its seasonal variation - SARIMA, within the exact and natural agricultural sciences, engineering, computing and others.

引用

页码：145 / 150

页数：6

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