Module 4: Time Series

4. Module 4: Time Series#

4.1. Objectives#

Equip participants with practical skills to analyze and interpret time series data using Python libraries.

4.2. Learning Outcomes#

By the end of this module, participants will be able to:

  • Understand the structure of time series data.

  • Preprocess temporal data and apply resampling techniques.

  • Visualize trends and decompose time series.

  • Forecast using statistical (ARIMA) and machine learning models (Prophet).

  • Evaluate model performance.

4.3. Topics#

  • Introduction to time series and use cases

  • Working with datetime objects

  • Resampling, rolling windows

  • Decomposition into trend, seasonality, residual

  • Visualization techniques

  • Forecasting with ARIMA, exponential smoothing

  • Forecasting with Prophet

  • Use case: TBD

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