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  3. Introduction to Linear Modeling in Python

Exercise

Interpolation: Inbetween Times

In this exercise, you will build a linear model by fitting monthly time-series data for the Dow Jones Industrial Average (DJIA) and then use that model to make predictions for daily data (in effect, an interpolation). Then you will compare that daily prediction to the real daily DJIA data.

A few notes on the data. "OHLC" stands for "Open-High-Low-Close", which is usually daily data, for example the opening and closing prices, and the highest and lowest prices, for a stock in a given day. "DayCount" is an integer number of days from start of the data collection.

Instructions

100 XP
  • Use ols() to .fit() a model to the data=df_monthly with formula="Close ~ DayCount".
  • Use model_fit.predict() on both df_monthly.DayCount and df_daily.DayCount to predict values for the monthly and daily Close prices, stored as a new column Model in each DataFrame.
  • Use the predefined plot_model_with_data twice, on each df_monthly and df_daily and compare the RSS values shown.