  In time series forecasting, decomposition-based algorithms break aggregate
data into meaningful components and are therefore appreciated for their
particular advantages in interpretability. Recent algorithms often combine
machine learning (hereafter ML) methodology with decomposition to improve
prediction accuracy. However, incorporating ML is generally considered to
sacrifice interpretability inevitably. In addition, existing hybrid algorithms
usually rely on theoretical models with statistical assumptions and focus only
on the accuracy of aggregate predictions, and thus suffer from accuracy
problems, especially in component estimates. In response to the above issues,
this research explores the possibility of improving accuracy without losing
interpretability in time series forecasting. We first quantitatively define
interpretability for data-driven forecasts and systematically review the
existing forecasting algorithms from the perspective of interpretability.
Accordingly, we propose the W-R algorithm, a hybrid algorithm that combines
decomposition and ML from a novel perspective. Specifically, the W-R algorithm
replaces the standard additive combination function with a weighted variant and
uses ML to modify the estimates of all components simultaneously. We
mathematically analyze the theoretical basis of the algorithm and validate its
performance through extensive numerical experiments. In general, the W-R
algorithm outperforms all decomposition-based and ML benchmarks. Based on
P50_QL, the algorithm relatively improves by 8.76% in accuracy on the practical
sales forecasts of JD.com and 77.99% on a public dataset of electricity loads.
This research offers an innovative perspective to combine the statistical and
ML algorithms, and JD.com has implemented the W-R algorithm to make accurate
sales predictions and guide its marketing activities.
