Orange3 TimeFeatures
TimeFeatures is an add-on for Orange 3 data mining software for generating synthetic data using datasets with time series, generating graphs of relationships between the generated variables, and includes widgets to save and load data and configuration tables from a database.
Features
- 🕐 7 time-window functions —
shift,sum,mean,min,max,count,sdwith full chunk-boundary correctness - 🔗 Chained descriptors — derived variables can reference each other; topological sort resolves the evaluation order automatically
- 🧮 Python-style expressions — evaluated with a restricted
eval(__builtins__replaced, curated whitelist only). This is not a security sandbox: expressions can still run arbitrary code, so only open workflows from sources you trust - 🗄️ PostgreSQL & MySQL — persist and reload datasets via SQLAlchemy with dialect-agnostic SQL generation
- 📊 Directed weighted dependency graphs — edge weights reflect temporal window size; visualise in Network Explorer
- ⚡ Bulk upload performance — pandas
DataFrame.to_sqlwith chunked multi-row INSERTs - 💾 Workflow persistence — variable definitions survive save/reload without clicking Send first
Widgets
| Widget | Description |
|---|---|
| Time Features Constructor | Defines new variables from existing ones using Python-style expressions and time-window functions. Supports chained descriptors with automatic topological sorting. |
| Variable Dependency Graph | Builds a directed, weighted dependency graph from the variable definitions. Edge weights summarise how far back or forward in time each variable looks. |
| Save to DB | Persists the resulting dataset to a SQL database (PostgreSQL or MySQL), with full SQL-injection defences and three write modes (create / overwrite / append). |
| Load from DB | Lists datasets previously stored by Save to DB and pulls the chosen one back into Orange, optionally marking the class column directly so no Select Columns widget is needed. |
Installation
Orange add-on installer
Install from Orange add-on installer through Options -> Add-ons.
Using pip
To install the add-on with pip use
pip install TimeFeatures
To install the add-on from source in editable mode, run
pip install -e .
Anaconda
If using Anaconda Python distribution, simply run
pip install TimeFeatures
Required Dependencies (Python 3.10 or newer):
- Orange3>=3.36.2
- numpy>=1.22.4
- AnyQt>=0.2.0
- PyQt5>=5.15.6
- PyQtWebEngine>=5.15.6
- scipy>=1.7.3
- SQLAlchemy>=1.4.0
- psycopg2-binary>=2.9.9
- PyMySQL>=1.0.0
- Orange3-Network>=1.8.0
Usage
After the installation, the widgets from this add-on are registered with Orange. To run Orange from the terminal, use
orange-canvas
or
python3 -m Orange.canvas
New widgets are in the toolbox bar under Time-Features section.
Documentation
The add-on includes Sphinx documentation for each widget. Orange resolves the local HTML pages through its internal Help panel, not through an internet URL. To rebuild the documentation locally, run
pip install -e ".[docs]"
python -m sphinx -b html docs docs/build/html
The bundled in-app help is pre-built under timefeatures/help_html/. To
regenerate it (e.g. after editing the .rst files), run
python -m sphinx -b html docs timefeatures/help_html
Use the widget help action in Orange to open the corresponding page inside the Orange Help window.
Workflow Example
This is an example of how you can use this add-on.
Metadata
Release files for TimeFeatures 2.3.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| timefeatures-2.3.0.tar.gz | 1.5 MB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| timefeatures-2.3.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 2.0 MB
Release files / timefeatures-2.3.0.tar.gz
| Download URL | timefeatures-2.3.0.tar.gz |
|---|---|
| Size | 1.5 MB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
0a5741472dbbe8a98b1e9b8994f189402c32db77e40dcb2a193695a3214e867b
|
|
BLAKE2b-256 checksum How to use checksums |
89f360b86ff5852c02d1850bef6e5170a37a35ce8d744f6aca7ca99663768612
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Sep 23, 2026.
Transparency logRelease files / timefeatures-2.3.0-py3-none-any.whl
| Download URL | timefeatures-2.3.0-py3-none-any.whl |
|---|---|
| Size | 550.9 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
5ea445bcede527c66a3c3006982002c00e0bae46f40c4111f4e7cea4c5bc905d
|
|
BLAKE2b-256 checksum How to use checksums |
893655c17cd8f3424e9d1d31614da1a9b6d4a183da62af953ebb5daab26cfdf7
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Sep 23, 2026.
Transparency log