TIME SERIES ANALYSIS OF HEAT STROKE
Rashmi Bhardwaj(1)* and Varsha Duhoon(2)
(1)*University School of Basic and Applied Sciences, Non-linear Dynamics Research Lab,
Guru Gobind Singh Indraprastha University, Sec-16C, Dwarka, New Delhi-110078, India
Email:rashmib22@gmail.com
(2)University School of Basic and Applied Sciences, Non-linear Dynamics Research Lab,
Guru Gobind Singh Indraprastha University, Sec-16C, Dwarka,New Delhi-110078, India
Email:varshaduhoon5@gmail.com
(Received : January 24, 2018 ; Revised: March 26, 2019)
Abstract
India in terms of Travel and tourism has been ranked at the 40th globally as per the list of World Economic Forum. Tourism is one of the main source of earning in our country, as it helps in Earning of Foreign Exchange from Tourism (PR) in terms of INR (1 crore=10 million), 135193 crores, Rate of Annual Growth 9.6%, in US$ terms Billion US $ 21.07, Rate of Annual Growth 4.1%, which means a lot for the country and holds good share in GDP of India. The amount of carbon dioxide released by the public transports such as buses, cars and air ways are affecting the air in the atmosphere highly. The rapid increase in the temperature causing deaths due to heat stroke, thus, it is important to take preventive measures to reduce the emission of carbon dioxide to control global warming. The paper studies about the change in the predictability of the temperature using time series analysis for the factors which may affect chaotic situation leading to the increase in death rates due to heat stroke also studying the regression analysis and descriptive statistics of the data of both tourism and temperature. Increase in temperature causes hazardous change in environment in form of Global Warming. Anti persistence behavior is observed which is alarming due to the chaotic nature thus government may take directives as emphasized by global initiative like Paris Climate Agreement 2015 in direction of controlling the carbon emission and reducing Global Warming.
2010 Mathematics Subject Classification: 37C45
Keywords and phrases: Time Series, Predictability Index, Global Warming, CO emission, Chaos.