MomentQuant：一种用于时间序列分类的更极简区间方法，具有线性时间复杂度

中文摘要 AI 辅助

时间序列数据在众多实际应用和领域中非常普遍，人们对使用机器学习进行自动信息提取的兴趣日益增长，时间序列分类是其中一个子领域，其任务是为每个新的、未见过的时间序列分配一个标签。过去几十年间已开发出许多算法，预测性能与计算成本之间的权衡一直是讨论的焦点。Quant是一种基于区间的算法，它从递归的固定二元区间中提取分位数，已被证明能实现高准确率且运行速度极快。本文提出两项改进，使该算法速度进一步提升：第一项是对完全相同算法的优化实现；第二项是使用Cornish-Fisher展开推导近似分位数，而非精确分位数，这一改动消除了对时间序列进行排序的必要性，从而降低了计算复杂度。我们将这种新算法命名为MomentQuant。研究结果表明，我们实现的Quant比原始Quant更快，而MomentQuant的速度甚至比我们实现的Quant更快，代价是预测性能略有下降。这些改进对于实际应用尤为重要，因为实际应用中推理的执行频率远高于训练。

英文摘要

Time series data is very common in many real-world applications and in numerous domains, with increasing interest for automated information extraction using machine learning. One of these subfields is time series classification, which consists in assigning a label to each new, unseen time series. Many algorithms have been developed over the past decades, with the trade-off between predictive performance and computational cost being consistently discussed. Quant, an interval-based algorithm extracting quantiles from recursive, fixed, dyadic intervals, was shown to achieve high accuracy, while being very fast. We propose two changes to make this algorithm even faster. The first one is a better optimized implementation of the exact same algorithm. The second one is to derive approximate quantiles, using the Cornish-Fisher expansion, instead of exact quantiles. This change removes the necessity to sort the time series, leading to a smaller computational complexity. We call this novel algorithm MomentQuant. We provide evidence that our implementation of Quant is faster than the original one, and that MomentQuant is even faster than our implementation of Quant, at the cost of a tiny decrease in predictive performance. These improvements are especially relevant for real-life applications, where inference is performed much more often than training.

发表机构

• Univ Rennes（雷恩大学）
• Ensai（国立统计与信息分析学院）
• CNRS（法国国家科学研究中心）
• CREST

机构由 AI 辅助整理，请以论文原文为准。