dsf-ts-forecasting

Time Series Forecasting in Python - Data Science Festival - GSK.
:tv: the workshop recording is available here -> https://online.datasciencefestival.com/talks/workshop/

Contents

• Time Series EDA
• Naive Benchmarks
• Evaluation metrics
• Time Series Cross Validation
• Statistical Methods - Exponential Smoothing, ARIMA, TBATS
• Machine Learning for time-series forecasting
  • direct approach
  • recursive features
  • global forecasting models

Quickstart

1. Create a python virtual environment:
   python -m venv .venv
2. Activate your environment:
   source .venv/bin/activate
3. If you want install the development requirements:
   pip install -r requirements.dev.txt
4. Install pre-commit to use pre-commit hooks: pre-commit install
5. Install the package in development mode:
   pip install -e .

OR

1. make environment
2. source .venv/bin/activate

Data

Data was downloaded from the CDC - Flu portal dashboard

Additional Resources

• VIDEOS:
  • Prof Rob Hyndman - Uncertain futures: what can we forecast and when should we give up?
  • Any video on the YouTube channel of the Centre for Marketing Analytics and Forecasting
  • Sktime on PyData
• BLOGS & WEBSITES:
  • Hyndsight
  • Kouretzes
  • International Institute of Forecasters
• BOOKS:
  • Forecasting: Principles and Practice (3rd ed)
  • Forecasting with Exponential Smoothing: the State Space Approach
  • Intermittent Demand Forecasting: Context, Methods and Applications
  • Time Series Analysis by State Space Methods

References

1. Hyndman, R.J., & Athanasopoulos, G. (2021) Forecasting: principles and practice, 3rd edition, OTexts: Melbourne, Australia. https://otexts.com/fpp3/