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.

This exercise is part of the course

Machine Learning for Time Series Data in Python

View Course

Exercise instructions

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.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Replace missing values with the median for each column
X = prices_perc_shifted.____(np.____(prices_perc_shifted))
y = prices_perc.____(np.___(prices_perc))

# Fit the model
model = Ridge()
model.fit(____, ____)

Edit and Run Code