发表机构
Nixtla（Nixtla）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文介绍 Nixtlaverse 开源预测生态系统，通过共享数据与输出契约统一统计、机器学习及神经模型，并在 M5 数据上验证其效率、可扩展性与协调能力。
AI 中文摘要
大型预测应用通常结合统计模型、机器学习模型和神经网络模型。这些模型家族解决相同的问题，但在拟合状态、训练流程以及并行化工作方式上有所不同。因此，预测软件要么将这些差异隐藏在一个统一的估计器接口之后，要么将各模型家族保留在独立的软件包中，迫使用户为每个软件包重写数据准备和评估流程。我们介绍 Nixtlaverse——一个用于时间序列预测的开源 Python 库生态系统，作为第三种设计方案的案例研究：所有库共享相同的长格式面板数据和带键的预测输出，而每个模型家族保留其专用的实现。我们通过公共 M5 竞赛数据上的三个用例来展示这一设计。首先，我们在单一的滚动起点评估中，结合按序列和按层级加权的指标，评估统计模型、机器学习模型、神经网络模型以及来自独立生态系统的外部引擎。其次，我们分析了从 100 到 30,490 个序列的运行时和峰值内存，并定位了每个模型家族的瓶颈：统计拟合的规模随序列数量近似线性增长，特征构建主导机器学习的内存使用，而在固定训练预算下，神经网络训练时间几乎与面板大小无关。第三，我们在 M5 层级的所有 42,840 个序列上，对多个引擎（包括外部引擎）的预测进行协调，采用稀疏协调方法，而密集实现则因内存耗尽而失败。这些用例确立了共享数据和输出契约的成本、边界和效用。Nixtlaverse 已获得大量的公开分发、学术复用，并通过其他预测框架被采纳，且以宽松的开源许可证发布，附带公共数据集、可复现示例和可验证的基准工件。
英文摘要
Large forecasting applications often combine statistical, machine-learning, and neural models. These families solve the same problem but differ in fitted state, training procedures, and how they parallelize work. Forecasting software must therefore either hide these differences behind a single estimator interface, or keep the families in separate packages, forcing users to rewrite data preparation and evaluation for every package. We present the Nixtlaverse, an ecosystem of open-source Python libraries for time series forecasting, as a case study of a third design: all libraries share the same long-format panel data and keyed forecast outputs, while every model family keeps its own specialized implementation. We demonstrate this design through three use cases on the public M5 competition data. First, we evaluate statistical, machine-learning, and neural models, and an external engine from a separate ecosystem, in a single rolling-origin evaluation with per-series and hierarchy-weighted metrics. Second, we profile runtime and peak memory from 100 to 30,490 series and locate each family's bottleneck: statistical fitting scales approximately linearly in the number of series, feature construction dominates machine-learning memory, and neural training time is nearly independent of panel size under a fixed training budget. Third, we reconcile the forecasts of multiple engines, including the external one, over all 42,840 series of the M5 hierarchy, with sparse reconciliation where dense implementations exhausted memory. These use cases establish the costs, boundaries, and utility of shared data and output contracts. The Nixtlaverse has seen substantial public distribution, scholarly reuse, and adoption through other forecasting frameworks, and is released under permissive open-source licenses with public datasets, reproducible examples, and verifiable benchmark artifacts.
Comments18 pages, 3 figures, 6 tables. Submitted to the International Journal of Forecasting. Code and benchmark artifact: https://doi.org/10.6084/m9.figshare.33399445
