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.