Timer-XL：用于统一时间序列预测的长上下文Transformer
Timer-XL: Long-Context Transformers for Unified Time Series Forecasting
AI总结：
提出Timer-XL，一种用于统一时间序列预测的因果Transformer，通过多元下一个token预测和TimeAttention机制，在多个预测基准上实现最先进性能并支持零样本预测。
摘要：
我们提出了Timer-XL，一种用于统一时间序列预测的因果Transformer。为了统一预测多维时间序列，我们将主要应用于一维token序列的下一个token预测，推广为多元下一个token预测。该范式将各种预测任务表述为一个长上下文预测问题。我们选择仅解码器的Transformer，它从不同长度的上下文中捕获因果依赖以进行统一预测，对非平稳单变量时间序列、具有复杂动态和相关性的多变量序列，以及包含外生变量的协变量知情上下文进行预测。在技术上，我们提出了通用的TimeAttention来捕获扁平化时间序列token（patches）的细粒度序列内和序列间依赖性，并通过用于时间因果性和变量等价性的巧妙位置嵌入进一步增强。Timer-XL通过统一方法在特定任务的预测基准上实现了最先进的性能。基于大规模预训练，Timer-XL实现了最先进的零样本性能，使其成为预训练时间序列模型的有前途的架构。代码在此仓库可用：https://github.com/thuml/Timer-XL。
英文摘要：
We present Timer-XL, a causal Transformer for unified time series forecasting. To uniformly predict multidimensional time series, we generalize next token prediction, predominantly adopted for 1D token sequences, to multivariate next token prediction. The paradigm formulates various forecasting tasks as a long-context prediction problem. We opt for decoder-only Transformers that capture causal dependencies from varying-length contexts for unified forecasting, making predictions on non-stationary univariate time series, multivariate series with complicated dynamics and correlations, as well as covariate-informed contexts that include exogenous variables. Technically, we propose a universal TimeAttention to capture fine-grained intra- and inter-series dependencies of flattened time series tokens (patches), which is further enhanced by deft position embedding for temporal causality and variable equivalence. Timer-XL achieves state-of-the-art performance across task-specific forecasting benchmarks through a unified approach. Based on large-scale pre-training, Timer-XL achieves state-of-the-art zero-shot performance, making it a promising architecture for pre-trained time series models. Code is available at this repository: https://github.com/thuml/Timer-XL.
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。
