Hello again.
Based on what we've learned so far we know that we avoid shuffling time series data because we want
to preserve the chronological order of the set.
Well one of the main reasons is that we often discover links between past and present values within
t series data namely we find some relationship between the entries for consecutive periods T and T minus
one.
All right.
Before we dive into the specifics let's define some important terms for starters correlation measures
the similarity in the change of values of two series.
In this case though we only have a single variable to calculate the similarity in a single time series
change through time.
We need to introduce a new concept auto correlation as the name suggests.
Auto correlation represents the correlation between a sequence and itself.
More precisely it measures the level of resemblance between a sequence from several periods ago and
the actual data.
Such a series is called lagged because it is essentially a delayed version of the original one for instance
if we find the auto correlation for a time series with daily frequency we are determining how much of
yesterday's values resemble today's values.
If the frequency is instead annual auto correlation will measure the similarities from year to year
fantastic since auto correlation is such a vital part of data analysis in the next video.
We will explore how to compute it.
Thanks for watching.