统一长期时间序列预测基准
Unified Long-Term Time-Series Forecasting Benchmark
AI总结：
提出一个专为长期时间序列预测设计的综合数据集，通过标准化轨迹和多种模型基准测试，发现模型效果依赖于数据集，并引入改进的潜在NLinear和课程学习DeepAR模型。
摘要：
为了支持时间序列数据预测的机器学习方法的发展，我们提出了一个明确针对长期时间序列预测设计的综合数据集。我们整合了来自多种动态系统和真实记录的数据集集合。每个数据集通过将数据划分为具有预定回溯长度的训练和测试轨迹进行标准化。我们包含长度高达$2000$的轨迹，以确保对长期预测能力的可靠评估。为了确定在不同场景中最有效的模型，我们使用经典和最先进的模型（即LSTM、DeepAR、NLinear、N-Hits、PatchTST和LatentODE）进行了广泛的基准分析。我们的研究结果揭示了这些模型之间有趣的性能比较，突出了模型有效性的数据集依赖性。值得注意的是，我们引入了一个自定义的潜在NLinear模型，并通过课程学习阶段增强了DeepAR。两者都持续优于其原始版本。
英文摘要：
In order to support the advancement of machine learning methods for predicting time-series data, we present a comprehensive dataset designed explicitly for long-term time-series forecasting. We incorporate a collection of datasets obtained from diverse, dynamic systems and real-life records. Each dataset is standardized by dividing it into training and test trajectories with predetermined lookback lengths. We include trajectories of length up to $2000$ to ensure a reliable evaluation of long-term forecasting capabilities. To determine the most effective model in diverse scenarios, we conduct an extensive benchmarking analysis using classical and state-of-the-art models, namely LSTM, DeepAR, NLinear, N-Hits, PatchTST, and LatentODE. Our findings reveal intriguing performance comparisons among these models, highlighting the dataset-dependent nature of model effectiveness. Notably, we introduce a custom latent NLinear model and enhance DeepAR with a curriculum learning phase. Both consistently outperform their vanilla counterparts.
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。
