MixBench-TS：一个通道混合有效的多变量时间序列预测基准
MixBench-TS: A Multivariate Time Series Forecasting Benchmark Where Channel Mixing Pays Off
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中文总结 AI 辅助
针对多变量时间序列预测中通道耦合评估不足的问题，提出MixBench-TS基准，包含10个真实数据集，并引入滞后MI和CD增益度量，证明现有标准数据集耦合弱，而新基准能更好评估通道混合模型。
中文摘要 AI 辅助
多变量时间序列预测（MTSF）模型在通道间混合信息，假设一个通道的过去携带关于另一个通道未来的信息。然而，它们在一个小型固定的标准数据集集合上进行评估，这些数据集的跨通道结构很少被检查。我们提出两个问题：“如何在MTSF数据集中可靠地测量滞后、非线性和联合耦合？”以及“标准数据集是否真的具有这种耦合？”为了回答第一个问题，我们在具有植入真实耦合的合成数据集上测试了四种候选度量：格兰杰因果（GC）、传递熵（TE）、滞后互信息（MI）以及我们引入的基于模型的CD增益，该增益将通道相关（CD）模型与其通道无关（CI）变体进行比较。只有滞后MI和CD增益能够恢复所有植入的耦合。对于第二个问题，答案是否定的，因为标准数据集的中位滞后耦合通道对比例仅为23%，中位CD增益为-4.9%，而混沌ODE系统分别为78%和+4.6%。因此，我们提出了MixBench-TS，一个包含10个真实世界数据集的基准，其中位滞后耦合对比例为55.5%，中位CD增益为+1.7%。在统一协议下调整的六个最先进模型中，CI模型在标准数据集上以10/10（MSE）和8/10（MAE）获胜，但在MixBench-TS数据集上仅以3/10和2/10获胜。我们建议使用我们的基准来评估新的CD模型。此外，我们建议在使用新数据集评估多变量模型之前，使用滞后MI和CD增益对其进行剖析。代码和数据可在https URL获取。
英文摘要
Multivariate Time Series Forecasting (MTSF) models that mix information across channels assume that the past of one channel carries information about the future of another. Yet they are evaluated on a small fixed set of standard datasets whose cross-channel structure is rarely examined. We ask two questions: "How can we reliably measure lagged, non-linear, and joint coupling in MTSF datasets?" and "Do the standard datasets actually have such coupling?" To answer the first, we test four candidate measures on synthetic datasets with planted ground-truth coupling: Granger Causality (GC), Transfer Entropy (TE), lagged Mutual Information (MI), and the CD gain, a model-based measure we introduce that compares a channel-dependent (CD) model to its channel-independent (CI) variant. Only lagged MI and the CD gain recover every planted coupling. For the second question, the answer is a definite no, as the standard datasets have a median of only 23% lagged-coupled channel pairs and a median CD gain of -4.9%, compared to 78% and +4.6% on chaotic ODE systems. We therefore propose MixBench-TS, a benchmark of 10 real-world datasets with a median of 55.5% lagged-coupled pairs and a median CD gain of +1.7%. Across six state-of-the-art models tuned under one protocol, CI models win on 10/10 (MSE) and 8/10 (MAE) standard datasets, but on only 3/10 and 2/10 MixBench-TS datasets. We recommend using our benchmark for evaluating new CD models. Moreover, we propose profiling new datasets with lagged MI and the CD gain before using them to evaluate multivariate models. Code and data are available at https://anonymous.4open.science/r/mixbench-ts-B027.
发表机构
- University of Hildesheim（希尔德斯海姆大学）
机构由 AI 辅助整理，请以论文原文为准。
