Exponential Smoothing State Space Innovation Model for Forecasting Road Accident Deaths in India

被引：0
|
作者：
Dutta, Bornali [1 ,2 ]
Barman, Manash Pratim [2 ]
Patowary, Arnab Narayan [3 ]
机构：
[1] Gargaon Coll, Dept Stat, Simaluguri, Assam, India
[2] Dibrugarh Univ, Dept Stat, Dibrugarh, Assam, India
[3] Assam Agr Univ, Coll Fisheries, Raha, Assam, India
来源：
THAILAND STATISTICIAN | 2022年 / 20卷 / 01期
关键词：
Akaike information criteria; Kolmogorov-Smirnov test; mean absolute percentage error; mean absolute scaled error;
D O I：
暂无
中图分类号：
O21 [概率论与数理统计]; C8 [统计学];
学科分类号：
020208 ; 070103 ; 0714 ;
摘要：
Road traffic accident in India is one of the country's major problems. With the use of statistical methods and models it is possible to predict the future occurrence of road traffic accidental deaths with the available data. This paper outlines the development of a conventional exponential smoothing state space innovation model for the annual deaths due to road accident in India covering the period 1967 to 2015 and to forecast the number of annual deaths likely to occur in future. The analyzed data are secondary in nature and obtained from National Crime Record Bureau, Ministry of Home Affairs. The researchers investigated and found that exponential smoothing state space model (A, A, N) is suitable for the historical data. The forecasted number of deaths from the model due to road accidents in India for the upcoming 10 years also exhibits an upward trend.
引用
收藏
页码：26 / 35
页数：10
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