Reads - Forecasting

Paper #1 - Hierarchical forecasting with a top-down alignment of independent level forecasts

Key Notes

• Deep learning forecasting approach N-BEATS for continuous-time series on top levels
• Tree-based algorithm LightGBM for the bottom level intermittent time series
• Split the time series into the non-zero component and stochastic component
• The lowest level of the hierarchy exhibits a strong intermittent pattern
• Upper hierarchy levels contain forecastable components such as the trend or seasonality aggregated by the lowest level

Paper #2 - Hierarchical Dynamic Modeling for Individualized Bayesian Forecasting

Key Notes

Models for personalized forecasting should be

•  able to incorporate predictor information such as price and promotions,
•  adaptable to time-varying trends, regression effects and unforeseen temporal changes,
•  interpretable and open to intervention by users and downstream decision makers,
•  fully probabilistic to properly characterize forecast uncertainties and allow formal model and forecast assessment under multiple metrics,
•  adapted to hierarchical settings, and amenable to automated, computationally efficient sequential learning and forecasting.

We define three household groups based on total items purchased over the course of the 112 weeks:

• Household Group 1: high spending and purchasing households
• Household Group 2: moderate spending and purchasing households
• Household Group 3: lower spending and purchasing households

Household Group, Proportion Return, Mean Spend, Median Spend, SD Spend

More Reads

Optimal Combination Forecasts on Retail Multi-Dimensional Sales Data

hts: An R Package for Forecasting Hierarchical or Grouped Time Series

Keep Thinking!!!