Time series prediction is a prevalent issue across various disciplines, such
as meteorology, traffic surveillance, investment, and energy production and
consumption. Many statistical and machine-learning strategies have been
developed to tackle this problem. However, these approaches either lack
explainability or exhibit less satisfactory performance when the prediction
horizon increases. To this end, we propose a novel plan for the designing of
networks' architecture based on transformations, possessing the potential to
achieve an enhanced receptive field in learning which brings benefits to fuse
features across scales. In this context, we introduce four different
transformation mechanisms as bases to construct the learning model including
Fourier Transform (FT), Singular Value Decomposition (SVD), matrix
multiplication and Conv block. Hence, we develop four learning models based on
the above building blocks, namely, FT-Matrix, FT-SVD, FT-Conv, and Conv-SVD.
Note that the FT and SVD blocks are capable of learning global information,
while the Conv blocks focus on learning local information. The matrix block is
sparsely designed to learn both global and local information simultaneously.
The above Transformation Learning Networks (TLNets) have been extensively
tested and compared with multiple baseline models based on several real-world
datasets and showed clear potential in long-range time-series forecasting.

提出了基于变换的网络架构设计计划，包括傅里叶变换、奇异值分解、矩阵乘法和卷积块。这些变换机制在学习中具有增强的可接受范围，可以跨尺度融合特征。经过多次测试比较，基于该设计计划的 TLNets 在长期时间序列预测方面表现有明显的潜力。

TLNets：用于长序列时间序列预测的转换学习网络

TLNets: Transformation Learning Networks for long-range time-series  prediction

Continuous-depth neural networks can be viewed as deep limits of discrete
neural networks whose dynamics resemble a discretization of an ordinary
differential equation (ODE). Although important steps have been taken to
realize the advantages of such continuous formulations, most current techniques
are not truly continuous-depth as they assume \textit{identical} layers.
Indeed, existing works throw into relief the myriad difficulties presented by
an infinite-dimensional parameter space in learning a continuous-depth neural
ODE. To this end, we introduce a shooting formulation which shifts the
perspective from parameterizing a network layer-by-layer to parameterizing over
optimal networks described only by a set of initial conditions. For
scalability, we propose a novel particle-ensemble parametrization which fully
specifies the optimal weight trajectory of the continuous-depth neural network.
Our experiments show that our particle-ensemble shooting formulation can
achieve competitive performance, especially on long-range forecasting tasks.
Finally, though the current work is inspired by continuous-depth neural
networks, the particle-ensemble shooting formulation also applies to
discrete-time networks and may lead to a new fertile area of research in deep
learning parametrization.

本文介绍了一种用粒子集成参数化的朝向初始条件进行连续深度神经网络优化的方法，并在长期预测任务中取得了相当竞争的性能。此外，该方法也适用于研究离散时间网络的新领域。