Delivering precise point and distributional forecasts across a spectrum of prediction horizons represents a significant and enduring challenge in the application of time-series forecasting within various industries. Prior research on developing deep learning models for time-series forecasting has often concentrated on isolated aspects, such as long-term point forecasting or short-term probabilistic estimations. This narrow focus may result in skewed methodological choices and hinder the adaptability of these models to uncharted scenarios. While there is a rising trend in developing universal forecasting models, a thorough understanding of their advantages and drawbacks, especially regarding essential forecasting needs like point and distributional forecasts across short and long horizons, is still lacking. In this paper, we present ProbTS, a benchmark tool designed as a unified platform to evaluate these fundamental forecasting needs and to conduct a rigorous comparative analysis of numerous cutting-edge studies from recent years. We dissect the distinctive data characteristics arising from disparate forecasting requirements and elucidate how these characteristics can skew methodological preferences in typical research trajectories, which often fail to fully accommodate essential forecasting needs. Building on this, we examine the latest models for universal time-series forecasting and discover that our analyses of methodological strengths and weaknesses are also applicable to these universal models. Finally, we outline the limitations inherent in current research and underscore several avenues for future exploration.

翻译：在多个行业的时间序列预测应用中，实现跨预测范围谱系的精确点预测与分布预测，是一个重要且持久的挑战。先前关于开发深度学习模型用于时间序列预测的研究，往往集中于孤立的方面，例如长期点预测或短期概率估计。这种狭隘的焦点可能导致方法论选择的偏差，并阻碍这些模型对未知场景的适应能力。尽管开发通用预测模型的趋势日益增长，但对其优势和缺点的全面理解，特别是在跨短期和长期范围的点预测与分布预测等基本预测需求方面，仍然缺乏。在本文中，我们提出了ProbTS，这是一个基准测试工具，旨在作为一个统一的平台来评估这些基本预测需求，并对近年来众多前沿研究进行严格的比较分析。我们剖析了由不同预测需求所产生的独特数据特征，并阐明了这些特征如何在典型的研究轨迹中导致方法论偏好的偏差，而这些轨迹往往未能充分适应基本的预测需求。在此基础上，我们研究了最新的通用时间序列预测模型，并发现我们对方法论优势和弱点的分析同样适用于这些通用模型。最后，我们概述了当前研究中固有的局限性，并强调了未来探索的几个方向。