Exercise

# Special case: Auto-regressive models

Now that you've created time-shifted versions of a single time series, you can fit an *auto-regressive* model. This is a regression
model where the input features are time-shifted versions of the output time series data. You are using previous values of a timeseries to predict current values of the same timeseries (thus, it is auto-regressive).

By investigating the coefficients of this model, you can explore any repetitive patterns that exist in a timeseries, and get an idea for how far in the past a data point is predictive of the future.

Instructions

**100 XP**

- Replace missing values in
`prices_perc_shifted`

with the median of the DataFrame and assign it to`X`

. - Replace missing values in
`prices_perc`

with the median of the series and assign it to`y`

. - Fit a regression model using the
`X`

and`y`

arrays.