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

Authors

  • Bornali Dutta Department of Statistics, Gargaon College, Simaluguri, Assam, India
  • Manash Pratim Barman Department of Statistics, Dibrugarh University, Dibrugarh, Assam, India
  • Arnab Narayan Patowary College of Fisheries, Assam Agricultural University, Raha, Assam, India

Keywords:

Akaike information criteria, Kolmogorov-Smirnov test, mean absolute percentage error, mean absolute scaled error

Abstract

Now-a-days, road traffic accident increases day by day and becomes burning problem in India. With the use of statistical methods and models it is possible to predict the future occurrence of road accident or deaths with the available data. The present study talk about the development of a exponential smoothing state space innovation model for the annual deaths due to road accident in India considering the period from 1967 to 2015 and to forecast the number of annual deaths expected to occur in forthcoming days. The researchers’ collected data from National Crime Record Bureau, Ministry of Home Affairs, India. After examining all the probable models, it is observed that exponential smoothing state space model (A, A, N) is suitable for the given data set. Further, study also shows that forecasted number of deaths for the upcoming 10 years from the proposed model also reveals an upward trend.

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Published

2021-12-30

How to Cite

Dutta, B. ., Pratim Barman, M. ., & Narayan Patowary, A. . (2021). Exponential Smoothing State Space Innovation Model for Forecasting Road Accident Deaths in India. Thailand Statistician, 20(1), 26–35. Retrieved from https://ph02.tci-thaijo.org/index.php/thaistat/article/view/245847

Issue

Vol. 20 No. 1 (2022): January

Section

Articles