# ts2vec

**Repository Path**: tslnihao/ts2vec

## Basic Information

- **Project Name**: ts2vec
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2026-03-03
- **Last Updated**: 2026-03-03

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# TS2Vec

This repository contains the official implementation for the paper [TS2Vec: Towards Universal Representation of Time Series](https://arxiv.org/abs/2106.10466) (AAAI-22).

## Requirements

The recommended requirements for TS2Vec are specified as follows:
* Python 3.8
* torch==1.8.1
* scipy==1.6.1
* numpy==1.19.2
* pandas==1.0.1
* scikit_learn==0.24.2
* statsmodels==0.12.2
* Bottleneck==1.3.2

The dependencies can be installed by:
```bash
pip install -r requirements.txt
```

## Data

The datasets can be obtained and put into `datasets/` folder in the following way:

* [128 UCR datasets](https://www.cs.ucr.edu/~eamonn/time_series_data_2018) should be put into `datasets/UCR/` so that each data file can be located by `datasets/UCR/<dataset_name>/<dataset_name>_*.csv`.
* [30 UEA datasets](http://www.timeseriesclassification.com) should be put into `datasets/UEA/` so that each data file can be located by `datasets/UEA/<dataset_name>/<dataset_name>_*.arff`.
* [3 ETT datasets](https://github.com/zhouhaoyi/ETDataset) should be placed at `datasets/ETTh1.csv`, `datasets/ETTh2.csv` and `datasets/ETTm1.csv`.
* [Electricity dataset](https://archive.ics.uci.edu/ml/datasets/ElectricityLoadDiagrams20112014) should be preprocessed using `datasets/preprocess_electricity.py` and placed at `datasets/electricity.csv`.
* [Yahoo dataset](https://webscope.sandbox.yahoo.com/catalog.php?datatype=s&did=70) should be preprocessed using `datasets/preprocess_yahoo.py` and placed at `datasets/yahoo.pkl`.
* [KPI dataset](http://test-10056879.file.myqcloud.com/10056879/test/20180524_78431960010324/KPI%E5%BC%82%E5%B8%B8%E6%A3%80%E6%B5%8B%E5%86%B3%E8%B5%9B%E6%95%B0%E6%8D%AE%E9%9B%86.zip) should be preprocessed using `datasets/preprocess_kpi.py` and placed at `datasets/kpi.pkl`.


## Usage

To train and evaluate TS2Vec on a dataset, run the following command:

```train & evaluate
python train.py <dataset_name> <run_name> --loader <loader> --batch-size <batch_size> --repr-dims <repr_dims> --gpu <gpu> --eval
```
The detailed descriptions about the arguments are as following:
| Parameter name | Description of parameter |
| --- | --- |
| dataset_name | The dataset name |
| run_name | The folder name used to save model, output and evaluation metrics. This can be set to any word |
| loader | The data loader used to load the experimental data. This can be set to `UCR`, `UEA`, `forecast_csv`, `forecast_csv_univar`, `anomaly`, or `anomaly_coldstart` |
| batch_size | The batch size (defaults to 8) |
| repr_dims | The representation dimensions (defaults to 320) |
| gpu | The gpu no. used for training and inference (defaults to 0) |
| eval | Whether to perform evaluation after training |

(For descriptions of more arguments, run `python train.py -h`.)

After training and evaluation, the trained encoder, output and evaluation metrics can be found in `training/DatasetName__RunName_Date_Time/`. 

**Scripts:** The scripts for reproduction are provided in `scripts/` folder.


## Code Example

```python
from ts2vec import TS2Vec
import datautils

# Load the ECG200 dataset from UCR archive
train_data, train_labels, test_data, test_labels = datautils.load_UCR('ECG200')
# (Both train_data and test_data have a shape of n_instances x n_timestamps x n_features)

# Train a TS2Vec model
model = TS2Vec(
    input_dims=1,
    device=0,
    output_dims=320
)
loss_log = model.fit(
    train_data,
    verbose=True
)

# Compute timestamp-level representations for test set
test_repr = model.encode(test_data)  # n_instances x n_timestamps x output_dims

# Compute instance-level representations for test set
test_repr = model.encode(test_data, encoding_window='full_series')  # n_instances x output_dims

# Sliding inference for test set
test_repr = model.encode(
    test_data,
    causal=True,
    sliding_length=1,
    sliding_padding=50
)  # n_instances x n_timestamps x output_dims
# (The timestamp t's representation vector is computed using the observations located in [t-50, t])
```