Time Series Forecasting in Python

prerequisites
beginner Python • basics of pandas • basics of Matplotlib • basics of statsmodels • linear regression • basics of time series
skills learned
visualizing complex relationships between variables and across time • build linear regression and time series models (exponential smoothing, ARIMA) with statsmodels • adding intervention terms to time series models
Aric LaBarr
5 weeks · 6-10 hours per week · INTERMEDIATE
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Forecasting is one of the most useful techniques a data scientist can bring to an organization. By making predictions from complex data, they can guide policy and resource management and allow businesses to plan and prepare for the future.

In this liveProject, you’ll take on the role of a data scientist who’s been tasked with forecasting the future consumption of an energy company’s customers. Energy consumption is seasonal and complex, and so you’ll be using the powerful Python ecosystem to tame your data and make your predictions. Your challenge is to use regression and time series techniques to forecast and visualize hourly energy usage for millions of customers, and present your findings in a clear report to allow your bosses to oversee resource allocation.
This project is designed for learning purposes and is not a complete, production-ready application or solution.

book resources

When you start your liveProject, you get full access to the following books for 90 days.

project author

Aric LaBarr
Dr. Aric LaBarr is an Associate Professor of Analytics at the Institute for Advanced Analytics. There he helps design the innovative program to prepare a modern, data-driven work force. He teaches courses in predictive modeling, forecasting, simulation, financial analytics, and risk management. Previously, he was Director and Senior Scientist at Elder Research, where he mentored and led a team of data scientists and software engineers.

prerequisites

This liveProject is for intermediate Python programmers who know the basics of time series and forecasting techniques. To begin with this liveProject, you will need to be familiar with:

TOOLS
  • Basics of pandas
  • Basics of matplotlib
  • Basics of statsmodels
TECHNIQUES
  • Basics of linear regression
  • Basics of time series

you will learn

In this liveProject, you’ll master the lucrative skill of forecasting. This liveProject is focused on forecasting the energy industry, and the skills you develop can easily be transferred to finance, consumer product demand, and more.

  • Visualizing and exploring the time series characteristics of hourly energy data
  • Exploring relationships between temperature and energy usage across different times of year
  • Building a linear regression model to forecast energy usage
  • Expand the linear regression model to become more dynamic
  • Compare the dynamic time series model using ARIMA with an exponential smoothing model
  • Incorporate holiday effects into the time series forecast

features

Self-paced
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book resources
Get full access to select books for 90 days. Permanent access to excerpts from Manning products are also included, as well as references to other resources.
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