https://github.com/patrickzib/dictionary
WEASEL 2.0 - A Random Dilated Dictionary Transform for Fast, Accurate and Constrained Memory Time Series Classification
https://github.com/patrickzib/dictionary
classification dictionary supervised-learning time-series time-series-classification timeseries
Last synced: about 1 month ago
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WEASEL 2.0 - A Random Dilated Dictionary Transform for Fast, Accurate and Constrained Memory Time Series Classification
- Host: GitHub
- URL: https://github.com/patrickzib/dictionary
- Owner: patrickzib
- License: gpl-3.0
- Created: 2022-12-30T10:49:03.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2025-05-05T07:12:26.000Z (about 1 year ago)
- Last Synced: 2026-02-21T16:44:57.762Z (3 months ago)
- Topics: classification, dictionary, supervised-learning, time-series, time-series-classification, timeseries
- Language: Jupyter Notebook
- Homepage:
- Size: 8.41 MB
- Stars: 17
- Watchers: 1
- Forks: 4
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# WEASEL 2.0 - A Random Dilated Dictionary Transform for Fast, Accurate and Memory Constrained Time Series Classification
WEASEL 2.0 combines a novel dilation mapping, small dictionaries and hyper-parameter ensembling to obtain a fast, accurate, and constrained memory TSC. WEASEL 2.0 is significantly more accurate than its predecessor dictionary methods (BOSS, TDE, WEASEL), and in the same group as SotA non-ensemble methods.
ArXiv-Paper: https://arxiv.org/abs/2301.10194
The paper has been accepted within the journal track at ECML-PKDD 2023: https://link.springer.com/article/10.1007/s10994-023-06395-w
### Accuracy against dictionary classifiers
### Accuracy against SotA classifiers
### Runtime against SotA classifiers
## Installation
### Dependencies
```
aeon >= 0.1.0
```
# Installation
The easiest is to use pip to install weasel-classifier.
## a) Install using pip
```
pip install weasel-classifier
```
You can also install the project from source.
## b) Build from Source
First, download the repository.
```
git clone https://github.com/patrickzib/dictionary.git
```
Change into the directory and build the package from source.
```
pip install .
```
### Train a WEASEL 2.0 classifier
WEASEL v2 follows the aeon pipeline.
```python
from aeon.datasets import load_arrow_head
from weasel.classification.dictionary_based import WEASEL_V2
X_train, y_train = load_arrow_head(split="train", return_type="numpy3d")
X_test, y_test = load_arrow_head(split="test", return_type="numpy3d")
clf = WEASEL_V2(random_state=1379, n_jobs=4)
clf.fit(X_train,y_train)
clf.predict(X_test)
```
## AEON
WEASEL v2 is part of the `aeon` toolkit, too: https://github.com/aeon-toolkit/aeon
## Citing
If you use this algorithm or publication, please cite:
```bibtex
@article{schaefer2023weasel,
title={WEASEL 2.0: a random dilated dictionary transform for fast, accurate and memory constrained time series classification},
author={Sch{\"a}fer, Patrick and Leser, Ulf},
journal={Machine Learning},
volume={112},
number={12},
pages={4763--4788},
year={2023},
publisher={Springer}
}
```