Multivariate Time Series

Climate change is the greatest environmental challenge facing the world today. Rising global temperatures will bring changes in weather patterns, rising sea levels and increased frequency and intensity of extreme weather. The Yearly Weather data can be a great source to detect any climatic change in our country. This book contains a multivariate autoregressive analysis on temperature of Rajshahi district of Bangladesh. We try to apply a unique and suitable forecasting model for Temperature data. At first three well known statistical forecasting models; Multiple Regression Model, Autoregressive Integrated Moving Average (ARIMA) Model, Vector Autoregressive (VAR) Model are chosen. After analysis we find that VAR(2) best fit for the Temperature data. So the information is, for yearly temperature forecasting task in Rajshahi District the first choice might be VAR(2). Using all the above methods Temperature was forecasted for the out-of-sample period 2008-2021.The analysis should help shed some light on this new and exciting topic, and should be especially useful to professionals in Geography and Geology fields, or anyone else who may be considering Global warming as a serious issue.

Autorentext

Rifat Zahan Bachelor of Science (B.S.), Applied Statistics, Institute of Statistical Research and Training, University of Dhaka, Dhaka-1000, Bangladesh, June, 2009. Mrs. Yesmin Akhter Lecturer, Applied Statistics, Institute of Statistical Research and Training, University of Dhaka, Dhaka-1000, Bangladesh.