TARNet for the Classification of Time Series with Missing Values: A Strong Baseline

This repository contains the experiments for the paper TARNet for the Classification of Time Series with Missing Values: A Strong Baseline. This TARNet model focuses solely on the time series classification task, specifically where time series have missing values.

The code from this project is a modified version of the official PyTorch implementation of the KDD 2022 paper TARNet: Task-Aware Reconstruction for Time-Series Transformer.

To find the original implementation, please visit HERE.

🔃 Citation

If you find this code is useful, feel free to reference the following paper:

@article{Novak2022-wi,
  title = {TARNet for the classification of time series with missing values: a strong baseline},
  journal = { Acta Universitatis Sapientiae. Informatica.},
  volume = {16(1)},
  pages = {1-19},
  year = {2024},
  issn = {2066-7760},
  doi = {https://doi.org/10.47745/ausi-2024-0001},
  url = {https://acta.sapientia.ro/content/docs/info16-1-01-324608.pdf},
  author = {Krisztian Buza and Erik Novak},
}

☑️ Requirements

Before starting the project make sure these requirements are available:

• python. For setting up the environment and Python dependencies (version 3.8 or higher).
• git. For versioning your code.

🛠️ Setup

Create a python environment

First, create a virtual environment where all the modules will be stored.

Using venv

Using the venv command, run the following commands:

# create a new virtual environment
python -m venv venv

# activate the environment (UNIX)
source ./venv/bin/activate

# activate the environment (WINDOWS)
./venv/Scripts/activate

# deactivate the environment (UNIX & WINDOWS)
deactivate

Install

Check the requirements.txt file. If you have any additional requirements, add them here.

To install the requirements run:

pip install -e .

🗃️ Data

The datasets used to evaluate the model are found on the time series classification datasets. To prepare the datasets for the experiments, follow the steps:

1. Make a new data folder:

   mkdir -p data/raw

2. Navigate into the new data folder:

   cd data/raw

3. Download the datasets from the time series classification datasets and store them in the data/raw folder. An example on how to download the dataset is given below:

   wget -nc https://www.timeseriesclassification.com/aeon-toolkit/Adiac.zip
   unzip Adiac.zip -d Adiac

The datasets used in our experiments are:

Adiac, ArrowHead, Chinatown, ChlorineConcentration, CinCECGTorso, DiatomSizeReduction, Earthquakes, ECG200, ECG5000, ECGFiveDays, FaceAll, FiftyWords, FordA, FordB, Haptics, InlineSkate, ItalyPowerDemand, allat, MedicalImages, MelbournePedestrian, MoteStrain, OSULeaf, Phoneme, Plane, PowerCons, SonyAIBORobotSurface1, SonyAIBORobotSurface2, Strawberry, SwedishLeaf, Symbols, TwoLeadECG, Wafer, WordSynonyms, Yoga.

⚗️ Experiments

To run the experiments, run the following commands:

python ./scripts/train.py --dataset Adiac --lr 0.001 --batch 64 --epochs 100 --sparsity 0.8

The script will train and test the model results, storing the performance metrics in the results folder. The training and test will be performed using the 10-fold cross validation.

The input parameters of the train script are:

Parameter	Description
--dataset	The name of the dataset. Corresponds to the folder name in the data/raw folder.
--lr	The learning rate.
--batch	The batch size.
--epochs	The number of epochs.
--sparsity	The sparsity level. Value between 0 and 1.

NOTE: The current sparsification implementation is the batch sparsification. If you want to change it to random sparsification, change the sparsify_time_series_dataset method in src/utils.py.

If the user downloaded all of the good dataset candidates above, they can run the following script to run the experiment on all of the datasets:

bash ./scripts/train_all.sh

Furthermore, feel free to update the script to run the experiments only for a specific datasets.