DisenTS：面向多元时间序列预测的解耦通道演化模式建模
DisenTS: Disentangled Channel Evolving Pattern Modeling for Multivariate Time Series Forecasting
AI总结：
针对多元时间序列预测中统一建模难以适配不同通道演化模式的问题，提出DisenTS框架，通过多预测器解耦建模、FAG模块自适应路由及相似性约束优化，提升预测效果。
摘要：
多元时间序列预测在各类实际应用中发挥着关键作用。现有研究已大量整合先进网络架构与训练策略，以强化对时间依赖关系的捕捉，进而提升预测精度。另一方面，主流方法通常采用单一统一模型，借助简单的通道混合嵌入或跨通道注意力操作来处理关键且复杂的通道间依赖关系。此外，部分方法甚至基于通道独立假设，以牺牲模型容量为代价换取鲁棒预测。然而，由于各通道的独特特性（包括多重强季节性与趋势变化），时间序列数据可能呈现出截然不同的演化模式，统一建模方法可能产生次优结果。为此，我们提出DisenTS，这是一个专为通用多元时间序列预测中的解耦通道演化模式建模而设计的框架。DisenTS的核心思想是以解耦的方式对多元时间序列数据中潜在的多样模式进行建模。技术上，该框架采用多个不同的预测模型，每个模型负责挖掘一种独特的演化模式。为在无模式划分监督的情况下引导学习过程，我们提出了一种新型预测器感知门控（Forecaster Aware Gate, FAG）模块，该模块可根据预测器状态与输入序列特征自适应生成分派信号。预测器状态通过线性权重近似（Linear Weight Approximation, LWA）策略得到，该策略将复杂深度神经网络量化为紧凑矩阵。此外，我们进一步提出相似性约束（Similarity Constraint, SC），通过最小化表征间的互信息，引导每个模型专门学习一种潜在模式。
英文摘要：
Multivariate time series forecasting plays a crucial role in various real-world applications. Significant efforts have been made to integrate advanced network architectures and training strategies that enhance the capture of temporal dependencies, thereby improving forecasting accuracy. On the other hand, mainstream approaches typically utilize a single unified model with simplistic channel-mixing embedding or cross-channel attention operations to account for the critical intricate inter-channel dependencies. Moreover, some methods even trade capacity for robust prediction based on the channel-independent assumption. Nonetheless, as time series data may display distinct evolving patterns due to the unique characteristics of each channel (including multiple strong seasonalities and trend changes), the unified modeling methods could yield suboptimal results. To this end, we propose DisenTS, a tailored framework for modeling disentangled channel evolving patterns in general multivariate time series forecasting. The central idea of DisenTS is to model the potential diverse patterns within the multivariate time series data in a decoupled manner. Technically, the framework employs multiple distinct forecasting models, each tasked with uncovering a unique evolving pattern. To guide the learning process without supervision of pattern partition, we introduce a novel Forecaster Aware Gate (FAG) module that generates the routing signals adaptively according to both the forecasters' states and input series' characteristics. The forecasters' states are derived from the Linear Weight Approximation (LWA) strategy, which quantizes the complex deep neural networks into compact matrices. Additionally, the Similarity Constraint (SC) is further proposed to guide each model to specialize in an underlying pattern by minimizing the mutual information between the representations.
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