基于表格先验拟合网络的零样本多变量时间序列预测
Zero-shot Multivariate Time Series Forecasting Using Tabular Prior Fitted Networks
AI总结：
本文提出利用表格基础模型进行多变量时间序列预测，通过将问题转化为标量回归问题，实现零样本预测，使用TabPFN-TS验证方法并对比现有最佳表格方法。
摘要：
表格基础模型，特别是先验数据拟合网络如TabPFN，在各种任务中表现出色，超越了传统树模型的成果。本文探讨了其在时间序列预测中的应用，通过将多变量时间序列预测转化为标量回归问题，利用具备回归能力的表格基础模型实现零样本预测，并通过TabPFN-TS验证方法与现有最佳表格方法进行比较。
英文摘要：
Tabular foundation models, particularly Prior-data Fitted Networks like TabPFN have emerged as the leading contender in a myriad of tasks ranging from data imputation to label prediction on the tabular data format surpassing the historical successes of tree-based models. This has led to investigations on their applicability to forecasting time series data which can be formulated as a tabular problem. While recent work to this end has displayed positive results, most works have limited their treatment of multivariate time series problems to several independent univariate time series forecasting subproblems, thus ignoring any inter-channel interactions. Overcoming this limitation, we introduce a generally applicable framework for multivariate time series forecasting using tabular foundation models. We achieve this by recasting the multivariate time series forecasting problem as a series of scalar regression problems which can then be solved zero-shot by any tabular foundation model with regression capabilities. We present results of our method using the TabPFN-TS backbone and compare performance with the current state of the art tabular methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
