Deep time-series forecasting plays an integral role in numerous practical applications. However, existing research fall short by focusing narrowly on either neural architecture designs for long-term point forecasts or probabilistic models for short-term scenarios. By proposing a comprehensive framework, facilitated by a novel tool, ProbTS, that integrates diverse data scenarios, evaluation metrics, and methodological focuses, we aim to transcend the limitations of current forecasting practices. Rigorous experimentation uncovers pivotal insights, including the supreme importance of aligning forecasting methodologies with the unique characteristics of the data; the necessity of a broad spectrum of metrics for accurately assessing both point and distributional forecasts; and the challenges inherent in adapting existing forecasting methods to a wider range of scenarios. These findings not only challenge conventional approaches but also illuminate promising avenues for future research, suggesting a more nuanced and effective strategy for advancing the field of deep time-series forecasting.

翻译：深度时间序列预测在众多实际应用中发挥着核心作用。然而，现有研究存在局限，它们过于狭隘地聚焦于长期点预测的神经网络架构设计，或短期场景下的概率模型。通过提出一个综合框架（借助新型工具ProbTS实现），该框架整合了多样化的数据场景、评估指标和方法论重点，我们旨在超越当前预测实践的局限性。严格的实验揭示出关键见解，包括：将预测方法与数据的独特特征对齐至关重要；需要广泛的指标来准确评估点预测与分布预测；以及将现有预测方法适应更广泛场景时所固有的挑战。这些发现不仅挑战了传统方法，还指明了未来研究的有前景方向，提出了推动深度时间序列预测领域发展的更细致且有效的策略。