Introduction to the Course

Time Series Analysis in Python

Rob Reider

Adjunct Professor, NYU-Courant Consultant, Quantopian

Example of Time Series: Google Trends

Example of Time Series: Climate Data

Example of Time Series: Quarterly Earnings Data

Example of Multiple Series: Natural Gas and Heating Oil

Goals of Course

• Learn about time series models
• Fit data to a time series model
• Use the models to make forecasts of the future
• Learn how to use the relevant statistical packages in Python
• Provide concrete examples of how these models are used

Some Useful Pandas Tools

• Changing an index to datetime

    df.index = pd.to_datetime(df.index)
  

• Plotting data

    df.plot()
  

• Slicing data

    df['2012']
  

Some Useful Pandas Tools

• Join two DataFrames

    df1.join(df2)
  

• Resample data (e.g. from daily to weekly)

    df = df.resample(rule='W').last()
  

More pandas Functions

• Computing percent changes and differences of a time series

    df['col'].pct_change()
    df['col'].diff()
  

• pandas correlation method of Series

    df['ABC'].corr(df['XYZ'])
  

• pandas autocorrelation

    df['ABC'].autocorr()
  

Let's practice!

Time Series Analysis in Python