Time Series Forecasting

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This project is part of the liveProject series End-to-End Time Series Forecasting with Deep Learning
prerequisites
intermediate Python • basic SQL • intermediate data science • basics of deep learning
skills learned
serve our predictions with a REST API using FastAPI • validate the API query parameters by using Python’s type annotations and pydantic • track our model’s performance with MLflow
Jiahao Weng
1 week · 4-6 hours per week · INTERMEDIATE
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team

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Look inside

In this liveProject, you’ll architect a solution to serve predictions from time series forecasting models over a REST API. Once you’ve architected your solution from a high-level perspective, you’ll monitor and assess the performance of the model and potentially undertake retraining to improve accuracy.

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 SQL
  • Basic VS Code or other Python IDE
TECHNIQUES
  • Intermediate data science
  • Basics of deep learning

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.


  • Read and write data to PostgreSQL database with SQLAlchemy and pandas
  • Structure a code repository for easier code maintenance
  • Serve our predictions with a REST API using FastAPI
  • Validate the API query parameters by using Python’s type annotations and pydantic
  • Track our model’s performance with MLflow
  • Retrain, compare, and update models on a periodic basis

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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team

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  • five seats for your team
  • access to all Manning books, MEAPs, liveVideos, liveProjects, and audiobooks!
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  • choose twelve free products per year
  • exclusive 50% discount on all purchases
  • Deploy a Forecasting Model project for free