Temporal Seam Score for Assessing Continuity at Known Transitions in Time Series
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评估时间序列已知转换点连续性的时间接缝分数
- 研究问题
- 拼接不同观测或处理系统形成长期记录时，如何在已知转换点判断不连续程度，并区分其方向与相对异常程度？
- 方法与思路
- 作者先拟合不使用接缝信息的趋势与季节模型，再把转换点两侧对称窗口的残差差异，与各段内部匹配的参考差异比较。
- 材料陈述的贡献
- 摘要称，S 分数无需系统间时间重叠即可提供有方向的连续性诊断；模拟与植被记录应用展示其对相对断点幅度的反映。
- 阅读关注点
- 应核对窗口匹配、标准化尺度与可靠性标准，检查无法得到有限分数的站点原因，并区分观测系统变化和真实过程变化。
- 阅读边界
- 本资料为 arXiv 预印本，同行评审及期刊接收未核实；摘要未给出完整可靠性规则，该诊断本身不能确定不连续的因果来源。
原文摘要
Long environmental records increasingly combine observations from successive observing or processing systems. Transitions between these systems create temporal seams that may reflect imperfect harmonization or changes in the observed process. We introduce the Temporal Seam Score (S-score, or S), a signed, robust measure for assessing continuity at a known transition time. The complete time series is fitted with a seam-blind model accounting for trend, seasonality, and optional covariates. The residual contrast between symmetric windows around the transition is compared with matched reference contrasts calculated within the contributing segments. The S-score is the standardized departure of the seam contrast from this reference. An S-score near zero indicates that the seam is consistent with ordinary temporal variability. Its sign indicates the direction of the discontinuity, while its magnitude (|S|) indicates how exceptional the discontinuity is. Simulation showed that |S| reflects the imposed discontinuity relative to residual variability: a 20% shift produced a median |S| of 2.58 at a residual coefficient of variation of 0.10, compared with 0.84 at 0.50. In a MODIS-to-Sentinel-3 vegetation record, 121 of 500 sites yielded finite S-scores, of which 90 met all reliability criteria. Among these reliable sites, median S was +0.217 and median |S| was 0.627, indicating little systematic directional displacement and a median seam departure below one reference spread. The S-score provides a general and auditable diagnostic for evaluating temporal continuity across generations of sensors, algorithms, and observing systems without requiring temporal overlap, thereby supporting harmonization quality assurance and more reliable attribution analyses based on long-term environmental records.