PyData Amsterdam 2026

In the Netherlands, groundwater level time-series stored across partially overlapping databases need to be harmonised before they can be used in hydrogeological models for studying and predicting Dutch hydrology. Manual harmonisation does not scale: the search space contains millions of time-series, and a false match can propagate into model inputs. In this setting, missing a match is often less harmful than accepting the wrong one.

That makes this a certainty-first matching problem rather than a task of retrieving the closest match. Spatial proximity is not sufficient in dense monitoring networks, and a single similarity score breaks down under common artifacts such as vertical shifts and gaps introduced or removed during quality control. More generic approaches, including spectral representations and learned embeddings, can also fail when they compress away details that remain critical in the time-series.

We compare complementary matching scores based on metadata fingerprinting, Dynamic Time Warping, and point-wise value comparison. We found that no method dominates across archetypes: each succeeds on some data pathologies and fails on others. We show how these failure modes can in turn be used to design a more reliable matching workflow.

Attendees working on time-series matching, record linkage, or other large-scale data harmonisation problems applied to real-world data shaped by natural variability, human error, and imperfect observation histories, will leave with a practical framework for deciding when to trust different matching scores and how to combine them into an interpretable, scalable workflow that favors certainty over forced decisions.