diff --git a/episodes/01-introduction.md b/episodes/01-introduction.md
index d380834..1eac88c 100644
--- a/episodes/01-introduction.md
+++ b/episodes/01-introduction.md
@@ -12,17 +12,65 @@ exercises: 10
 
 ::::::::::::::::::::::::::::::::::::: objectives
 
-- Identify different use cases for time-series forecasting.
 - Define concepts applicable to forecasting models.
 
 ::::::::::::::::::::::::::::::::::::::::::::::::
 
 ## Introduction
 
+This lesson is the second in a series of lessons demonstrating Python
+libraries and methods for time-series analysis and forecasting. 
 
+The first lesson, 
+[Time Series Analysis of Smart Meter Power Consmption Data](https://carpentries-incubator.github.io/python-pandas-power-consumption/), 
+introduces datetime indexing features in the Python ```Pandas``` library. Other
+topics in the lesson include grouping data, resampling by time frequency, and 
+plotting rolling averages. 
 
-::::::::::::::::::::::::::::::::::::: keypoints
+This lesson introduces forecasting time-series. Specifically, this lesson aims
+to progressively demonstrate the attributes and processes of the 
+SARIMAX model (**S**easonal **A**uto-**R**egressive **I**ntegrated **M**oving **A**verage withe**X**ogenous factors). Exogenous factors are out of scope of
+the lesson, which is structured around the process of predicting a single 
+timestep of a variable based on the the historic values of that same variable.
+Multi-variate forecasts are not addressed. Relevant topics include:
+
+- Stationary and non-stationary time-series
+- Auto-regression
+- Seasonality
+
+The lesson demonstrates statistical methods for testing for the presence of 
+stationarity and auto-regression, and for using the ```SARIMAX``` class of the 
+Python ```statsmodels``` library to make forecasts that account for these
+processes. 
+
+As noted throughout the lesson, the code used in this lesson is based on and
+in some cases is a direct application of code used in the Manning Publications 
+title, *Time series forecasting in Python*, by Marco Peixeiro.
+
+> Peixeiro, Marco. Time Series Forecasting in Python. [First edition]. Manning Publications Co., 2022.
 
+The original code from the book is made available under an 
+[Apache 2.0 license](https://github.com/marcopeix/TimeSeriesForecastingInPython/blob/master/LICENSE.txt). Use and application of the code in these materials is within
+the license terms, although this lesson itself is licensed under a Creative Commons
+[CC-BY 4.0 license](https://creativecommons.org/licenses/by/4.0/legalcode). Any
+further use or adaptation of these materials should cite the source code
+developed by Peixeiro:
+
+> Peixeiro, Marco. Timeseries Forecasting in Python [Software code]. 2022.
+Accessed from [https://github.com/marcopeix/TimeSeriesForecastingInPython](https://github.com/marcopeix/TimeSeriesForecastingInPython).
+
+The third lesson in the series is 
+[Machine Learning for Timeseries Forecasting with Python](https://carpentries-incubator.github.io/python-classifying-power-consumption/).
+It follows from and builds upon concepts from these first two lessons in the 
+series.
+
+All three lessons use the same data. For information about the data and how to
+set up the environment so the code will work without the need to edit file paths,
+see the [Setup](https://carpentries-incubator.github.io/python-modeling-power-consumption/) section.
+
+::::::::::::::::::::::::::::::::::::: keypoints
 
+- The Python ```statsmodels``` library includes a full featured implementation
+of the SARIMAX model.
 
 :::::::::::::::::::::::::::::::::::::::::::::::