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  3. Machine Learning for Time Series Data in Python

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.