Multivariate time series forecasting has seen widely ranging applications in
various domains, including finance, traffic, energy, and healthcare. To capture
the sophisticated temporal patterns, plenty of research studies designed
complex neural network architectures based on many variants of RNNs, GNNs, and
Transformers. However, complex models are often computationally expensive and
thus face a severe challenge in training and inference efficiency when applied
to large-scale real-world datasets. In this paper, we introduce LightTS, a
light deep learning architecture merely based on simple MLP-based structures.
The key idea of LightTS is to apply an MLP-based structure on top of two
delicate down-sampling strategies, including interval sampling and continuous
sampling, inspired by a crucial fact that down-sampling time series often
preserves the majority of its information. We conduct extensive experiments on
eight widely used benchmark datasets. Compared with the existing
state-of-the-art methods, LightTS demonstrates better performance on five of
them and comparable performance on the rest. Moreover, LightTS is highly
efficient. It uses less than 5% FLOPS compared with previous SOTA methods on
the largest benchmark dataset. In addition, LightTS is robust and has a much
smaller variance in forecasting accuracy than previous SOTA methods in long
sequence forecasting tasks.

本文介绍了 LightTS，一种基于简单的 MLP 结构和两种精细的下采样策略的轻量级深度学习架构，用于多元时间序列预测，它具有高效、鲁棒和与先前 SOTA 方法相比更好的性能优势。

轻采样导向的 MLP 结构在多元时间序列快速预测中的应用

Less Is More: Fast Multivariate Time Series Forecasting with Light Sampling-oriented MLP Structures

The stability of word embedding algorithms, i.e., the consistency of the word
representations they reveal when trained repeatedly on the same data set, has
recently raised concerns. We here compare word embedding algorithms on three
corpora of different sizes, and evaluate both their stability and accuracy. We
find strong evidence that down-sampling strategies (used as part of their
training procedures) are particularly influential for the stability of
SVDPPMI-type embeddings. This finding seems to explain diverging reports on
their stability and lead us to a simple modification which provides superior
stability as well as accuracy on par with skip-gram embeddings.

比较不同语料库中词嵌入算法的稳定性和准确性，发现下采样策略特别影响 SVDPPMI 型嵌入的稳定性，通过简单修改可以提高稳定性和与 skip-gram 嵌入一样的准确性。