Week 1

This class introduces time series and its applications. Topics include:

  • What time series is and why it is important
  • How to decompose trend, seasonality, and residuals
  • What additive, multiplicative, and pseudo-additive models are
  • The application of time series forecasting with Python

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Week 2

This class introduces stationarity and its mathematical transformations. It includes:

  • The definition of stationarity and its relevance
  • Transformation methods such as differencing, detrending, and logarithms
  • How to differentiate nonstationarity and stationarity data with Python

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Week 3

This class teaches about data smoothing methods and their applications. Learn about:

  • Why data smoothing is essential for data analysis
  • Data smoothing techniques—from simple average to triple exponential smoothing
  • How to smooth time series data with Python

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Week 4

This class explains autocorrelation and partial autocorrelation. Topics include:

  • What autocorrelation and partial autocorrelation functions are and how they work
  • The variations of models such as autoregressive and moving average models
  • How to use Python to build autocorrelation models

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Week 5

This class introduces ARMA, ARIMA, and SARIMA models. Topics include:

  • How ARMA, ARIMA, and SARIMA models work and how to build them
  • How to implement these models with Python

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Week 6

This class goes into further detail about advanced time series. Topics include:

  • How to use control charts for anomaly detection
  • An introduction and use case for Kalman filters

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Week 7

This class introduces signal transformations. Learn about:

  • Why signal transformations are useful for time series analysis
  • Techniques such as Fourier transformations, filters, and window functions

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Week 8

This class teaches how to use deep learning with time series analysis. Topics include:

  • An explanation of RNN and LSTM architectures
  • How to use Python to implement deep learning models for time series forecasting

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