At the heart of FuzzyDiary lies a decades-old mathematical idea known as fuzzy linguistic summarization, originally proposed by Ronald Yager in 1982. In this framework, statements take a protoform such as Q of the objects in R are S, where Q is a fuzzy quantifier like most, R a contextual qualifier such as in the afternoon, and S a linguistic term like low. Each instantiated statement carries a degree of truth, and in extended frameworks also measures of focus and simplicity, so the narrative is not merely plausible prose but a graded, quantifiable description of the data. Because these statements are produced deterministically, the same input signal and the same configuration always yield the same text, a property the developers consider non-negotiable in clinical settings where factual reliability cannot be compromised.

This determinism is what separates FuzzyDiary from the wave of generative approaches built on large language models. While LLMs can produce fluent summaries of monitoring data, the verbalization step may introduce unverified claims that cannot be traced back to the underlying observations. FuzzyDiary takes the opposite route: the domain knowledge is modeled explicitly in a human-readable YAML configuration, and the narrative layer resolves only the presentation of facts that protoform evaluation has already established. The authors argue that a cohesive report does not require a language model when the semantics are fully specified, and they position the verified linguistic facts produced by their framework as grounded input that downstream language models can consume rather than compete with.

Technically, FuzzyDiary organizes the analysis as a pipeline of six core modules plus a reporting layer, each with a well-defined input and output contract that allows users to invoke the full chain or any intermediate stage in isolation. The io module ingests univariate series from CSV, JSON, or in-memory Pandas DataFrames, standardizes them into a canonical representation indexed by calendar day, and handles resampling, imputation of missing values, and segmentation into daily windows. The unit of description is deliberately the calendar day, matching the natural reporting rhythm of clinical practice and keeping the narrative aligned with how clinicians, patients, and caregivers reason about longitudinal monitoring data.

The next stage applies the Ramer-Douglas-Peucker simplification algorithm, a geometric technique borrowed from cartography, to reduce each daily window to a compact piecewise-linear approximation. The tolerance parameter epsilon controls how closely the approximation follows the signal, and a built-in heuristic suggests a sensible default from the observed variance. Over this simplified substrate, the events module detects morphological events such as peaks, valleys, and prolonged episodes, which are declared declaratively in the configuration rather than hard-coded in the source. Each event is parameterized by the linguistic terms it spans, its minimum and maximum duration, and the gaps that may be merged, and events can be anchored to or composed from previously detected ones, enabling domain-specific patterns such as recovery from low, a return to normal values after a drop.

The describe module then evaluates protoform templates against the detected events and aggregated daily statistics, producing ranked statements with truth degrees. Because the terms of a fuzzy linguistic variable overlap by design, a single sample may hold membership in more than one term, and several statements can describe the same stretch of signal without anything in the output expressing how they relate. FuzzyDiary separates what is said from how it is said through a synthesis stage that orders summarizers and quantifiers by the centroids of their membership functions, attaches more specific statements below the general ones that cover them, and nests detected events under others that specialize them and contain them in time. The synthesis reorganizes the evidence but never discards it, keeping the complete list of instantiated protoforms with their degrees of truth in the report for verification.

A generation stage then renders the synthesized hierarchy as a cohesive paragraph through document planning, aggregation, lexicalization, and template-based surface realization. Document planning visits contextual scopes in the order declared in the configuration, making the narrative chronological, and assigns each scope a rhetorical relation such as rise, fall, or continuation by comparing the rank of its main summarizer with that of the preceding scope. Aggregation merges adjacent scopes described by the same quantifier and summarizer, while elision removes what the sentence has already said, so sibling statements sharing the quantifier of the main one are coordinated rather than repeated. Every generated clause retains the statements and events it summarizes, so the narrative is as traceable as the graded list beneath it.

The final artifact is a self-contained interactive HTML report in which the plotting library is embedded directly in the generated file, requiring no server and no network access at opening time. Hovering over a sentence reveals the statements and events it was built from, and clicking it highlights the corresponding segments on the signal panel, zooming the view to frame that interval. Clauses resting on day-level statements alone are linked to the temporal support of their contextual qualifier, so no sentence of the report is left without a visual counterpart. The developers demonstrated the pipeline on real continuous glucose monitoring data from the T1GDUJA dataset, collected with the approval of the Ethics Committee of the University of Jaén, with the linguistic variable, event catalog, and protoform templates defined together with clinical experts in diabetes care.

The authors see impact along several dimensions. By exposing morphological event detection and fuzzy protoform evaluation as composable modules driven by a single YAML configuration validated through a Pydantic schema, FuzzyDiary lowers the barrier to empirical work on linguistic descriptions of time series, making previously ad-hoc research questions tractable through configuration-only experiments. For practitioners in clinical reporting, ambient monitoring, industrial telemetry, or any longitudinal sensor stream, it offers a deterministic, traceable alternative to bespoke reporting scripts. Although clinical monitoring is the motivating use case, the framework is domain-agnostic by design: any univariate signal with a meaningful daily structure is handled by the same modules, and moving to a new domain requires only a new configuration, not a change to the source code.

Future releases will extend FuzzyDiary along two axes: support for multivariate signals through joint protoform templates, and a richer discourse layer that extends the rhetorical relations and aggregation rules to the comparison of several reporting periods. Integration with large language models is deliberately left outside the framework, since embedding one would trade the determinism and reproducibility of the output for variation in phrasing alone. The current version targets univariate signals at daily granularity, and the developers note that a structured evaluation of the usability of the reports with clinical professionals is left for future work. For now, the framework fills a concrete gap between tabular fuzzy linguistic summarization libraries and numerical time-series toolkits, occupying a previously uncovered niche in the open-source ecosystem for interpretable clinical data analysis.

Subject of Research: FuzzyDiary open-source framework for traceable linguistic summarization of clinical time series

Article Title: FuzzyDiary: An open-source framework for morphology-aware and traceable linguistic summarization of clinical time series

Article References: Gaitán-Guerrero, J. F., López-Ruiz, J. L., Martínez-Cruz, C., & Espinilla, M. (2026). FuzzyDiary: An open-source framework for morphology-aware and traceable linguistic summarization of clinical time series. SoftwareX, 36, Article 103135. https://doi.org/10.1016/j.softx.2026.103135

Image Credits: AI Generated

DOI: 10.1016/j.softx.2026.103135

Keywords: FuzzyDiary, linguistic summarization, clinical time series, fuzzy logic, traceability, open-source software, explainable AI, natural language generation, open-source, framework, morphology-aware, traceable