  It is important to predict how the Global Mean Temperature (GMT) will evolve
in the next few decades. The ability to predict historical data is a necessary
first step toward the actual goal of making long-range forecasts. This paper
examines the advantage of statistical and simpler Machine Learning (ML) methods
instead of directly using complex ML algorithms and Deep Learning Neural
Networks (DNN). Often neglected data transformation methods prior to applying
different algorithms have been used as a means of improving predictive
accuracy. The GMT time series is treated both as a univariate time series and
also cast as a regression problem. Some steps of data transformations were
found to be effective. Various simple ML methods did as well or better than the
more well-known ones showing merit in trying a large bouquet of algorithms as a
first step. Fifty-six algorithms were subject to Box-Cox, Yeo-Johnson, and
first-order differencing and compared with the absence of them. Predictions for
the annual GMT testing data were better than that published so far, with the
lowest RMSE value of 0.02 $^\circ$C. RMSE for five-year mean GMT values for the
test data ranged from 0.00002 to 0.00036 $^\circ$C.
