Demand forecasts are the crucial basis for numerous business decisions,
ranging from inventory management to strategic facility planning. While machine
learning (ML) approaches offer accuracy gains, their interpretability and
acceptance are notoriously lacking. Addressing this dilemma, we introduce
Hierarchical Neural Additive Models for time series (HNAM). HNAM expands upon
Neural Additive Models (NAM) by introducing a time-series specific additive
model with a level and interacting covariate components.
Covariate interactions are only allowed according to a user-specified
interaction hierarchy. For example, weekday effects may be estimated
independently of other covariates, whereas a holiday effect may depend on the
weekday and an additional promotion may depend on both former covariates that
are lower in the interaction hierarchy.
Thereby, HNAM yields an intuitive forecasting interface in which analysts can
observe the contribution for each known covariate. We evaluate the proposed
approach and benchmark its performance against other state-of-the-art machine
learning and statistical models extensively on real-world retail data. The
results reveal that HNAM offers competitive prediction performance whilst
providing plausible explanations.

通过引入层次神经加法模型（HNAM），本研究解决了机器学习预测方法准确性高但解释性差的问题。HNAM 是一种针对时间序列的加法模型，允许根据用户指定的交互层次来进行协变量交互。通过对真实世界的零售数据进行广泛评估并与其他最先进的机器学习和统计模型进行比较，实验结果表明，HNAM 不仅具备竞争性的预测性能，还能提供可信的解释。

可解释需求预测的分层神经附加模型

Hierarchical Neural Additive Models for Interpretable Demand Forecasts

Fashion retailers require accurate demand forecasts for the next season,
almost a year in advance, for demand management and supply chain planning
purposes. Accurate forecasts are important to ensure retailers' profitability
and to reduce environmental damage caused by disposal of unsold inventory. It
is challenging because most products are new in a season and have short life
cycles, huge sales variations and long lead-times. In this paper, we present a
novel product age based forecast model, where product age refers to the number
of weeks since its launch, and show that it outperforms existing models. We
demonstrate the robust performance of the approach through real world use case
of a multinational fashion retailer having over 300 stores, 35k items and
around 40 categories. The main contributions of this work include unique and
significant feature engineering for product attribute values, accurate demand
forecast 6-12 months in advance and extending our approach to recommend product
launch time for the next season. We use our fashion assortment optimization
model to produce list and quantity of items to be listed in a store for the
next season that maximizes total revenue and satisfies business constraints. We
found a revenue uplift of 41% from our framework in comparison to the
retailer's plan. We also compare our forecast results with the current methods
and show that it outperforms existing models. Our framework leads to better
ordering, inventory planning, assortment planning and overall increase in
profit for the retailer's supply chain.

本文介绍了一种基于产品年龄的预测模型，对于时尚零售商的需求预测、供应链规划和库存管理都具有重要价值，通过实际案例验证了其在 6-12 个月内准确预测销售额的能力，并可以为下一季度的产品推出时间提供建议，此外还提供了产品组合优化模型，最大化收益并满足业务约束条件，相较于现有方法具有更好的预测效果和可操作性。

时尚零售产品基于产品年龄的需求预测模型

Product age based demand forecast model for fashion retail

Accurate demand forecasts can help on-line retail organizations better plan
their supply-chain processes. The challenge, however, is the large number of
associative factors that result in large, non-stationary shifts in demand,
which traditional time series and regression approaches fail to model. In this
paper, we propose a Neural Network architecture called AR-MDN, that
simultaneously models associative factors, time-series trends and the variance
in the demand. We first identify several causal features and use a combination
of feature embeddings, MLP and LSTM to represent them. We then model the output
density as a learned mixture of Gaussian distributions. The AR-MDN can be
trained end-to-end without the need for additional supervision. We experiment
on a dataset of an year's worth of data over tens-of-thousands of products from
Flipkart. The proposed architecture yields a significant improvement in
forecasting accuracy when compared with existing alternatives.

本研究提出了一种称为 AR-MDN 的神经网络结构，该结构能够同时模拟关联因素、时间序列趋势和需求方差，为在线零售组织提供精确的需求预测，同时也在消除需求方面存在的问题方面获得了显著的改进和提高。