Time Stamps and Financial Modeling - Doing Data Science - page 159

y = F t = α 0 + α 1 F t −1 + α 2 F t −2 +ϵ
which is just the example where we take the last two values of the time
series F to predict the next one. We could use more than two values,
of course. If we used lots of lagged values, then we could strengthen
our prior in order to make up for the fact that we've introduced so
many degrees of freedom. In effect, priors reduce degrees of freedom .
The way we'd place the prior about the relationship between coeffi‐
cients (in this case consecutive lagged data points) is by adding a matrix
to our covariance matrix when we perform linear regression. See more
about this here .
A Baby Model
Say we drew a plot in a time series and found that we have strong but
fading autocorrelation up to the first 40 lags or so as shown in
Figure 6-14 .
Figure 6-14. Looking at auto-correlation out to 100 lags
We can calculate autocorrelation when we have time series data. We
create a second time series that is the same vector of data shifted by a