Amazon ships billions of packages to its customers annually within the United
States. Shipping cost of these packages are used on the day of shipping (day 0)
to estimate profitability of sales. Downstream systems utilize these days 0
profitability estimates to make financial decisions, such as pricing strategies
and delisting loss-making products. However, obtaining accurate shipping cost
estimates on day 0 is complex for reasons like delay in carrier invoicing or
fixed cost components getting recorded at monthly cadence. Inaccurate shipping
cost estimates can lead to bad decision, such as pricing items too low or high,
or promoting the wrong product to the customers. Current solutions for
estimating shipping costs on day 0 rely on tree-based models that require
extensive manual engineering efforts. In this study, we propose a novel
architecture called the Rate Card Transformer (RCT) that uses self-attention to
encode all package shipping information such as package attributes, carrier
information and route plan. Unlike other transformer-based tabular models, RCT
has the ability to encode a variable list of one-to-many relations of a
shipment, allowing it to capture more information about a shipment. For
example, RCT can encode properties of all products in a package. Our results
demonstrate that cost predictions made by the RCT have 28.82% less error
compared to tree-based GBDT model. Moreover, the RCT outperforms the
state-of-the-art transformer-based tabular model, FTTransformer, by 6.08%. We
also illustrate that the RCT learns a generalized manifold of the rate card
that can improve the performance of tree-based models.

提出了一种名为 Rate Card Transformer（RCT）的新型架构，用于编码包裹的各种航运信息，并且在成本预测方面表现出比树模型和当前基于 Transformer 的 tabular 模型更高的预测准确性。

揭示自注意力对运费预测的威力：费率卡变换器

Unveiling the Power of Self-Attention for Shipping Cost Prediction: The  Rate Card Transformer

The state-of-the-art in time series classification has come a long way, from
the 1NN-DTW algorithm to the ROCKET family of classifiers. However, in the
current fast-paced development of new classifiers, taking a step back and
performing simple baseline checks is essential. These checks are often
overlooked, as researchers are focused on establishing new state-of-the-art
results, developing scalable algorithms, and making models explainable.
Nevertheless, there are many datasets that look like time series at first
glance, but classic algorithms such as tabular methods with no time ordering
may perform better on such problems. For example, for spectroscopy datasets,
tabular methods tend to significantly outperform recent time series methods. In
this study, we compare the performance of tabular models using classic machine
learning approaches (e.g., Ridge, LDA, RandomForest) with the ROCKET family of
classifiers (e.g., Rocket, MiniRocket, MultiRocket). Tabular models are simple
and very efficient, while the ROCKET family of classifiers are more complex and
have state-of-the-art accuracy and efficiency among recent time series
classifiers. We find that tabular models outperform the ROCKET family of
classifiers on approximately 19% of univariate and 28% of multivariate datasets
in the UCR/UEA benchmark and achieve accuracy within 10 percentage points on
about 50% of datasets. Our results suggest that it is important to consider
simple tabular models as baselines when developing time series classifiers.
These models are very fast, can be as effective as more complex methods and may
be easier to understand and deploy.

本研究比较了使用传统机器学习方法的表格模型与 ROCKET 系列分类器在时间序列分类中的性能，发现表格模型在大约 19％的单变量和 28％的多变量数据集上优于 ROCKET 系列分类器，并在约 50％的数据集上取得了不超过 10 个百分点的准确率，结果表明在开发时间序列分类器时要考虑简单的表格模型作为基线模型。

回归基础：对现代时间序列分类算法进行合理检验

Back to Basics: A Sanity Check on Modern Time Series Classification  Algorithms

Within enterprises, there is a growing need to intelligently navigate data
lakes, specifically focusing on data discovery. Of particular importance to
enterprises is the ability to find related tables in data repositories. These
tables can be unionable, joinable, or subsets of each other. There is a dearth
of benchmarks for these tasks in the public domain, with related work targeting
private datasets. In LakeBench, we develop multiple benchmarks for these tasks
by using the tables that are drawn from a diverse set of data sources such as
government data from CKAN, Socrata, and the European Central Bank. We compare
the performance of 4 publicly available tabular foundational models on these
tasks. None of the existing models had been trained on the data discovery tasks
that we developed for this benchmark; not surprisingly, their performance shows
significant room for improvement. The results suggest that the establishment of
such benchmarks may be useful to the community to build tabular models usable
for data discovery in data lakes.

在数据湖之中，企业内部需要智能地进行数据发现，特别是关于找到相关的数据表。该文提出了使用不同数据源的表格来开发多个用于这些任务的基准测试，并比较了 4 个公开可用的表格模型在这些任务上的表现，结果显示这些模型在数据发现任务上有很大的改进空间。建立这样的基准测试对于社区构建适用于数据湖中数据发现的表格模型可能是有用的。