Note that when we use the
trailing window when computing the moving average
of present values from t minus 32, t minus one. But when we use
a centered window to compute the moving average of
past values from one year ago, that's t minus one year
minus five days, to t minus one year
plus five days. Then moving averages
using centered windows can be more accurate than
using trailing windows. But we can't use
centered windows to smooth present values since we
don't know future values. However, to smooth past values we can afford to use
centered windows. Okay, so now we've looked at a few statistical methods for predicting the next values
in a time series. In the next video,
you'll take a look at a screencast of
this prediction in action. Once you've done
the statistical forecasting, the next step of
course will be to apply the machine-learning
techniques you've been learning all along in TensorFlow and you'll
do that next week.