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Hierarchical robust aggregation of sales forecasts at aggregated levels in e-commerce, based on exponential smoothing and Holt's linear trend method
We revisit the interest of classical statistical techniques for sales forecasting like exponential smoothing and extensions thereof (as Holt's linear trend method).
Online Hierarchical Forecasting for Power Consumption Data
Our approach consists in three steps: feature generation, aggregation and projection.
Uniform regret bounds over $R^d$ for the sequential linear regression problem with the square loss
In the case of sequentially revealed features, we also derive an asymptotic regret bound of $d B^2 \ln T$ for any individual sequence of features and bounded observations.
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