Forecasting Brazilian mortality rates due to occupational accidents using autoregressive moving average approaches

被引:26
|
作者
Melchior, Cristiane [1 ]
Zanini, Roselaine Ruviaro [1 ]
Guerra, Renata Rojas [1 ]
Rockenbach, Dinei A. [2 ]
机构
[1] Univ Fed Santa Maria UFSM, Ave Roraima 1000, BR-97105900 Santa Maria, RS, Brazil
[2] Pontifical Catholic Univ Rio Grande Sul PUCRS, Sch Technol, 32nd Bldg,Ave Ipiranga 6681, BR-90619900 Porto Alegre, RS, Brazil
关键词
Fatal work-related accidents; ARIMA; beta ARMA; KARMA; Forecasting; Time series; BETA REGRESSION; NORMALITY; MODELS; STATE; TESTS;
D O I
10.1016/j.ijforecast.2020.09.010
中图分类号
F [经济];
学科分类号
02 ;
摘要
We examine the mortality rates due to occupational accidents of the three states in the southern region of Brazil using the autoregressive integrated moving average (ARIMA), beta autoregressive moving average (beta ARMA), and Kumaraswamy autoregressive moving average (KARMA) models to fit the data sets, considering monthly observations from 2000 to 2017. We compare them to identify the best predictive model for the southern region of Brazil. We also provide descriptive analysis, revealing the victims' vulnerability characteristics and comparing them between the states. A clear increase was seen in female participation in the labor market, but the number of deaths from occupational accidents did not increase by the same proportion. Moreover, the state of Parana stood out for having the highest mortality rate from work-related accidents. The fitted ARIMA and beta ARMA models using a 6-month time frame presented similar accuracy measurements, while KARMA performed the worst. (C) 2020 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.
引用
收藏
页码:825 / 837
页数:13
相关论文
共 50 条
  • [31] Forecasting of milk production of crossbred dairy cattle by Autoregressive Integrated Moving Average (ARIMA) model
    Sharma, Rohit
    Chaudhary, J. K.
    Kumar, Sanjeev
    Rewar, Ranjit
    Kumar, Sandeep
    INDIAN JOURNAL OF DAIRY SCIENCE, 2022, 75 (04): : 376 - 380
  • [32] Beta autoregressive moving average model selection with application to modeling and forecasting stored hydroelectric energy
    Cribari-Neto, Francisco
    Scher, Vinicius T.
    Bayer, Fabio M.
    INTERNATIONAL JOURNAL OF FORECASTING, 2023, 39 (01) : 98 - 109
  • [33] Forecasting air quality index data with autoregressive integrated moving average models
    Vatresia, Arie
    Nafila, Ridha
    Agwil, Winalia
    Utama, Ferzha Putra
    Shehab, Maryam
    EQA-INTERNATIONAL JOURNAL OF ENVIRONMENTAL QUALITY, 2025, 65 : 86 - 96
  • [34] Rainfall Forecasting by Using Autoregressive Integrated Moving Average, Single Input and Multi Input Transfer Function
    Saikhu, Ahmad
    Arifm, Agus Zainal
    Fatichah, Chastine
    PROCEEDINGS OF 2017 11TH INTERNATIONAL CONFERENCE ON INFORMATION & COMMUNICATION TECHNOLOGY AND SYSTEMS (ICTS), 2017, : 85 - 89
  • [35] Forecasting the number of vehicles thefts in Campinas/Brazil using a Generalized Linear Autoregressive Moving Average model
    Pala, Luiz Otavio de Oliveira
    Carvalho, Marcela de Marillac
    Safadi, Thelma
    ELECTRONIC JOURNAL OF APPLIED STATISTICAL ANALYSIS, 2022, 15 (01) : 110 - 122
  • [36] FORECASTING THE SEAWEED PRODUCTION IN TAWI-TAWI USING SEASONAL AUTOREGRESSIVE INTEGRATED MOVING AVERAGE MODEL
    Palahuddin, Raul A.
    Langamin, Danilo G.
    Artes, Rosalio G.
    ADVANCES AND APPLICATIONS IN STATISTICS, 2024, 91 (06) : 719 - 738
  • [37] Forecasting Philippines Imports and Exports Using Bayesian Artificial Neural Network And Autoregressive Integrated Moving Average
    Urrutia, Jackie D.
    Abdul, Alsafat M.
    Atienza, Jacky Boy E.
    PROCEEDINGS OF THE 8TH SEAMS-UGM INTERNATIONAL CONFERENCE ON MATHEMATICS AND ITS APPLICATIONS 2019: DEEPENING MATHEMATICAL CONCEPTS FOR WIDER APPLICATION THROUGH MULTIDISCIPLINARY RESEARCH AND INDUSTRIES COLLABORATIONS, 2019, 2192
  • [38] Forecasting financial time series using a methodology based on autoregressive integrated moving average and Taylor expansion
    Zhang, Guisheng
    Zhang, Xindong
    Feng, Hongyinping
    EXPERT SYSTEMS, 2016, 33 (05) : 501 - 516
  • [39] Forecasting occurrence of palm weevil Rhynchophorus palmarum L. (Coleoptera, Curculionidae) using Autoregressive Integrated Moving Average modeling
    Pacheco-Sanchez, Eduardo L.
    Guamani-Quimis, Lenin A.
    da Rosa, Cinara Ewerling
    Portalanza, Diego
    Mieles, Alejandro E.
    Garces-Fiallos, Felipe R.
    SCIENTIA AGROPECUARIA, 2023, 14 (02) : 171 - 178
  • [40] A Novel Hybrid Autoregressive Integrated Moving Average and Artificial Neural Network Model for Cassava Export Forecasting
    Pannakkong, Warut
    Van-Nam Huynh
    Sriboonchitta, Songsak
    INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS, 2019, 12 (02) : 1047 - 1061