timeseriesflattener

Spec sheet

Purpose

timeseriesflattener is a Python package for generating time-series features used as predictors in machine-learning models.Read 4 Oct 2026 · aarhus-psychiatry-research.github.io

Audience

The introductory tutorial says the package is especially helpful for complicated and irregular time series when training simple models.Read 4 Oct 2026 · aarhus-psychiatry-research.github.io

Support

The docs direct bug reports and feature requests to GitHub Issues and usage questions or general discussion to GitHub Discussions.Read 4 Oct 2026 · aarhus-psychiatry-research.github.io

Aggregations

Documented aggregators include count, earliest, latest, maximum, mean, minimum, slope, sum, unique count, and variance.Read 4 Oct 2026 · aarhus-psychiatry-research.github.io

Dataframes

The API accepts pandas or Polars dataframes for prediction-time and static frames.Read 4 Oct 2026 · aarhus-psychiatry-research.github.io

Feature types

The API documents temporal predictors, outcomes, boolean outcomes, static features, and time-delta features.Read 4 Oct 2026 · aarhus-psychiatry-research.github.io

Install

The official installation page instructs users to install the package with pip using `pip install timeseriesflattener`.Read 4 Oct 2026 · aarhus-psychiatry-research.github.io

Missing values

Feature specifications accept a fallback value for cases where the relevant look window has no data.Read 4 Oct 2026 · aarhus-psychiatry-research.github.io

Output

It converts irregular time series into a dataframe with one row per prediction time and columns for constructed features, aggregating values by an ID column.Read 4 Oct 2026 · aarhus-psychiatry-research.github.io

Parallel processing

The introductory tutorial says n_workers can parallelize operations across multiple cores.Read 4 Oct 2026 · aarhus-psychiatry-research.github.io

Prediction windows

Feature specifications let users set prediction times and lookbehind windows for predictors or lookahead windows for outcomes.Read 4 Oct 2026 · aarhus-psychiatry-research.github.io

Pricing and trial

The opened official documentation describes a Python package and installation instructions but states no price or free-trial terms.Read 4 Oct 2026 · aarhus-psychiatry-research.github.io

Publication

The package has a 2023 paper in the Journal of Open Source Software describing it as a Python package for summarizing features from medical time series.Read 4 Oct 2026 · joss.theoj.org

Text

The text tutorial demonstrates generating flattened predictors from pre-embedded text represented as a dataframe with entity IDs, timestamps, and embedding columns.Read 4 Oct 2026 · aarhus-psychiatry-research.github.io

Tutorials

The documentation provides downloadable Jupyter notebook tutorials that users can run locally.Read 4 Oct 2026 · aarhus-psychiatry-research.github.io

Where Specifiction places it

• Data Preparation Software#27 of 286.4