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Tech riderRev. 24 Sept 2026

timeseriesflattener

Ch 146.2of 27 Data Preparation Software
  1. 1Runs onAPI, Linux
  2. 2CostsNot stated by the maker
  3. 3Deploymentself hosted
2 lines stated Written from the maker's own pages: aarhus-psychiatry-research.github.io
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The timeseriesflattener homepage

Overview

timeseriesflattener is ranked #14 of 27 in data preparation software on Specifiction. It runs on API, Linux.

Compared on data preparation software

Free plan
Yesaarhus-psychiatry-research.github.io
Deployment
self_hostedaarhus-psychiatry-research.github.io

Facts

Purpose
timeseriesflattener is a Python package for generating time-series features used as predictors in machine-learning models.aarhus-psychiatry-research.github.io · 4 Oct 2026
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.aarhus-psychiatry-research.github.io · 4 Oct 2026
Prediction windows
Feature specifications let users set prediction times and lookbehind windows for predictors or lookahead windows for outcomes.aarhus-psychiatry-research.github.io · 4 Oct 2026
Aggregations
Documented aggregators include count, earliest, latest, maximum, mean, minimum, slope, sum, unique count, and variance.aarhus-psychiatry-research.github.io · 4 Oct 2026
Feature types
The API documents temporal predictors, outcomes, boolean outcomes, static features, and time-delta features.aarhus-psychiatry-research.github.io · 4 Oct 2026
Missing values
Feature specifications accept a fallback value for cases where the relevant look window has no data.aarhus-psychiatry-research.github.io · 4 Oct 2026
Text
The text tutorial demonstrates generating flattened predictors from pre-embedded text represented as a dataframe with entity IDs, timestamps, and embedding columns.aarhus-psychiatry-research.github.io · 4 Oct 2026
Dataframes
The API accepts pandas or Polars dataframes for prediction-time and static frames.aarhus-psychiatry-research.github.io · 4 Oct 2026
Parallel processing
The introductory tutorial says n_workers can parallelize operations across multiple cores.aarhus-psychiatry-research.github.io · 4 Oct 2026
Install
The official installation page instructs users to install the package with pip using `pip install timeseriesflattener`.aarhus-psychiatry-research.github.io · 4 Oct 2026
Tutorials
The documentation provides downloadable Jupyter notebook tutorials that users can run locally.aarhus-psychiatry-research.github.io · 4 Oct 2026
Support
The docs direct bug reports and feature requests to GitHub Issues and usage questions or general discussion to GitHub Discussions.aarhus-psychiatry-research.github.io · 4 Oct 2026
Audience
The introductory tutorial says the package is especially helpful for complicated and irregular time series when training simple models.aarhus-psychiatry-research.github.io · 4 Oct 2026
Pricing and trial
The opened official documentation describes a Python package and installation instructions but states no price or free-trial terms.aarhus-psychiatry-research.github.io · 4 Oct 2026
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.joss.theoj.org · 4 Oct 2026

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