Some Interval Time Series Models for Temperature Data in India

A. Srinivasulu¹ , B. Sarojamma² , P. Ramakrishna Reddy³ , S. Venkataramana Reddy⁴

1. Department of Statistics, S.V.University, Tirupati, India.
2. Department of Statistics, S.V.University, Tirupati, India.
3. Department of Statistics, S.D.H.R Degree & P.G college, Tirupati, India.
4. Department of Physics, S.V.University, Tirupati, India.

Section:Research Paper, Product Type: Isroset-Journal
Vol.5 , Issue.3 , pp.40-47, Jun-2018

CrossRef-DOI:   https://doi.org/10.26438/ijsrmss/v5i3.4047

Online published on Jun 30, 2018

Copyright © A. Srinivasulu, B. Sarojamma, P. Ramakrishna Reddy, S. Venkataramana Reddy . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

View this paper at   Google Scholar | DPI Digital Library

XML View     PDF Download

How to Cite this Paper

545 Views    362 Downloads    157 Downloads

Abstract :
The temperature is fluctuated by special climate changes during seasons in India. In this paper, we were taking temperature data from 2000 to 2016. Maximum and minimum temperature values for season wise i.e., Jan-Mar(Spring), Apr-June(Summer), July-Sep(Autumn), Oct-Dec(Winter) has to be taken throughout India. The organization of the work is divided into two parts, first part contains data from 2000 to 2010 as test group and second part contains from 2011 to 2016 as main group. For this maximum and minimum temperature seasonal data we apply nine models. Among the nine, seven are ARIMA models, the 8th one is Adaptive smoothing model and the last one is non linear model. In this paper, two measures of accuracy are used. They are Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The nine models are empirically tested using Maximum and Minimum temperature data of season wise in India.

Key-Words / Index Term :
Seasonal temperature data, Adaptive smoothing model, ARIMA models, MAE, RMSE

References :
[1] Grant Foster, Stefan Rahmstorf, “Global Temperature Evolution 1979-2010”, Environ. Res. Lett.6, 044022,2011.
[2] Grant Foster, Niamh Cahill, Stefan Rahmstorf, , “Global Temperature Evoluation: Recent Trends and Some Pitfalls” , Environmental Research Letters, 12 , 054001,2017.
[3] Chonggunag Li, Lu Zhang, Tao Xiong , Yukun Bao, Zhongyi Hu, “A Combination Method for Interval Forecasting of Agricultural Commodity Futures Prices”, Knowledge-Based Systems ,77,92-102,2015.
[4] Markus Glaser, Martin Weber, Thomas Langer, “ True Overconfidence in Interval Estimates: Evidence Based on a New Measure of Miscalibration”, journal of Behavioral Decision Making, 26:405-417,2012.
[5] Aidan McDermott, Francesca Dominici, Jonathan M. Samet, Scott L.Zeger, “On the Use of Generalized Additive Models in Time Series Studies of Air Pollution and Health”, American Journal of Epidemiology, vol. 156, No. 3,2002.
[6] Heather A. Walton, H. Ross Anderson, Inga C. Mills, Richard W. Atkinson, “Fine Paricle Componens and Health- a Systematic review and Meta-Analysis of Epidemological Time Series Studies of Daily Mortality and Hospital Admissions”, Journal of Exposure Science and Environmental Epidemiology,25,208-214,2015.
[7] Alesssandro Baccini, Alexndra Tyukavina, Belinda Arunarwati Margono, Ilona Zhuravleva, Matthew C Hansen Peter Potapov, Scott Goetz, Svetlana Turubanova, “Mapping and Monitoring Deforestation and Forest Degradation in Sumatra (Indonesia) using Land sat time series data sets from 1990 to 2010”, Environ. Res. Lett.7, 034010, 2012.
[8] http//:www.data.gov.in.