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Time Series Forecasting

Prepare Time Series Data you own this product

This project is part of the liveProject series End-to-End Time Series Forecasting with Deep Learning
prerequisites
intermediate Python • intermediate data science • basic Google Colab
skills learned
build baseline models with Naive and sNaive methods • set up and optimize a Prophet model as an alternative model for comparison • evaluate various models using weighted MAE and MAPE
Jiahao Weng
1 week · 8-10 hours per week · INTERMEDIATE
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liveProject This project is part of the liveProject series End-to-End Time Series Forecasting with Deep Learning liveProjects give you the opportunity to learn new skills by completing real-world challenges in your local development environment. Solve practical problems, write working code, and analyze real data—with liveProject, you learn by doing. These self-paced projects also come with full liveBook access to select books for 90 days plus permanent access to other select Manning products. $19.99 $29.99 you save $10 (33%)
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In this liveProject, you’ll create baseline models with Naive and sNaive methods for time series forecasts that you can use as a point of comparison for other models. Taking on the role of a data scientist for a large retail company, you’ll go hands-on to prepare sales data, create the baseline models, and optimize a Prophet model to compare against your baseline.

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

Jiahao Weng
Jiahao Weng is a machine learning practitioner and a senior data scientist at a multinational company where he delivers projects ranging from proof-of-concept to production machine learning systems. As a freelance writer, he also contributes data science Medium articles to share his knowledge with the community.

prerequisites

The liveProject series is for intermediate data scientists interested in tackling their first end-to-end machine learning project. To begin this liveProject, you will need to be familiar with the following:


TOOLS
  • Intermediate Python
  • Basic Google Colab
  • Basic Jupyter Notebook
TECHNIQUES
  • Basics of data visualization
  • Basics of data science

you will learn

In this liveProject, you’ll tackle different areas of forecasting and model building. The skills you learn are the same kind used to solve complex problems by forecasters and data scientists in the industry.


  • Process and clean time series data with outlier detection and linear interpolation
  • Analyze and visualize time series with Seaborn and Plotly charts
  • Determine seasonality with periodogram
  • Train-test split time series data using nested walk-forward validation
  • Establish baseline models with Naive and sNaive methods
  • Set up and optimize a Prophet model as an alternative model for comparison
  • Evaluate various models using weighted MAE and MAPE

features

Self-paced
You choose the schedule and decide how much time to invest as you build your project.
Project roadmap
Each project is divided into several achievable steps.
Get Help
While within the liveProject platform, get help from other participants and our expert mentors.
Compare with others
For each step, compare your deliverable to the solutions by the author and other participants.
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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