Transformer-based and MLP-based methods have emerged as leading approaches in
time series forecasting (TSF). While Transformer-based methods excel in
capturing long-range dependencies, they suffer from high computational
complexities and tend to overfit. Conversely, MLP-based methods offer
computational efficiency and adeptness in modeling temporal dynamics, but they
struggle with capturing complex temporal patterns effectively. To address these
challenges, we propose a novel MLP-based Adaptive Multi-Scale Decomposition
(AMD) framework for TSF. Our framework decomposes time series into distinct
temporal patterns at multiple scales, leveraging the Multi-Scale Decomposable
Mixing (MDM) block to dissect and aggregate these patterns in a residual
manner. Complemented by the Dual Dependency Interaction (DDI) block and the
Adaptive Multi-predictor Synthesis (AMS) block, our approach effectively models
both temporal and channel dependencies and utilizes autocorrelation to refine
multi-scale data integration. Comprehensive experiments demonstrate that our
AMD framework not only overcomes the limitations of existing methods but also
consistently achieves state-of-the-art performance in both long-term and
short-term forecasting tasks across various datasets, showcasing superior
efficiency. Code is available at https://github.com/TROUBADOUR000/AMD

我们提出了一种新颖的基于 MLP 的自适应多尺度分解 (AMD) 框架用于时间序列预测，该框架在多个尺度上将时间序列分解为不同的时间模式，并利用多尺度分解混合 (MDM) 模块将这些模式进行拆分和聚合，通过双重依赖交互 (DDI) 模块和自适应多预测合成 (AMS) 模块有效地建模时间和通道依赖性，并利用自相关性来提炼多尺度数据集成。全面的实验表明，我们的 AMD 框架不仅克服了现有方法的局限性，而且在各种数据集上的长期和短期预测任务中始终取得了最先进的性能，展示了出色的效率。

时间序列预测的自适应多尺度分解框架

Adaptive Multi-Scale Decomposition Framework for Time Series Forecasting

We introduce Position Adaptive Convolution (PAConv), a generic convolution
operation for 3D point cloud processing. The key of PAConv is to construct the
convolution kernel by dynamically assembling basic weight matrices stored in
Weight Bank, where the coefficients of these weight matrices are
self-adaptively learned from point positions through ScoreNet. In this way, the
kernel is built in a data-driven manner, endowing PAConv with more flexibility
than 2D convolutions to better handle the irregular and unordered point cloud
data. Besides, the complexity of the learning process is reduced by combining
weight matrices instead of brutally predicting kernels from point positions.
Furthermore, different from the existing point convolution operators whose
network architectures are often heavily engineered, we integrate our PAConv
into classical MLP-based point cloud pipelines without changing network
configurations. Even built on simple networks, our method still approaches or
even surpasses the state-of-the-art models, and significantly improves baseline
performance on both classification and segmentation tasks, yet with decent
efficiency. Thorough ablation studies and visualizations are provided to
understand PAConv. Code is released on this https URL

引入了 Position Adaptive Convolution (PAConv) 算法，该算法通过使用 ScoreNet 从点位置自适应学习基础权重矩阵，并动态组装构建卷积核，从而实现更好处理不规则和无序点云数据的能力，在不改变网络配置的情况下将其集成到传统的基于多层感知器的点云管道中。