Open Access   Article

Statistical Analysis on Global Temperature Anomalies

S. Harsha1 , S. M. Fernandes2

Section:Research Paper, Product Type: Journal Paper
Volume-6 , Issue-6 , Page no. 60-68, Jun-2018


Online published on Jun 30, 2018

Copyright © S. Harsha, S. M. Fernandes . 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.

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IEEE Style Citation: S. Harsha, S. M. Fernandes, “Statistical Analysis on Global Temperature Anomalies”, International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.60-68, 2018.

MLA Style Citation: S. Harsha, S. M. Fernandes "Statistical Analysis on Global Temperature Anomalies." International Journal of Computer Sciences and Engineering 6.6 (2018): 60-68.

APA Style Citation: S. Harsha, S. M. Fernandes, (2018). Statistical Analysis on Global Temperature Anomalies. International Journal of Computer Sciences and Engineering, 6(6), 60-68.

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Temperature affects the smallest details of our daily life from what we wear to how we get to work to what we eat for lunch. Seldom can we go even a day without needing to know what the temperature is or will be. And we know that these days the temperature has been rising steadily around us and across the globe as well, thus we intended to make a study on Global temperature. The data set which we used in this paper is from the National Oceanic and Atmospheric Administration (NOAA). We have the Global temperature anomaly with respect to land and ocean from the year 1880 to 2017. Statistical techniques like Descriptive Statistics to summarize the data, Cluster Analysis to form clusters of the years that show similar kind of temperature variation, Correlation Analysis to understand the related variation between Land and Ocean temperature anomaly were carried out. Further Double Exponential Smoothing (Holt) model and ARIMA model is fitted to forecast the Land and Ocean temperature anomaly using the training set and there after the accuracy of the forecasted models has been compared by using Mean Absolute Error (MAE) and Mean Absolute Percent Error (MAPE). Finally, the model that has more accuracy is used to forecast the temperature anomaly for the year 2018 and 2019.

Key-Words / Index Term

Temperature anomaly, Chernoff face, Clusters, ARIMA and Holt model


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