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https://github.com/zhihanyue/ts2vec

A universal time series representation learning framework
https://github.com/zhihanyue/ts2vec

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A universal time series representation learning framework

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# 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//_*.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//_*.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   --loader  --batch-size  --repr-dims  --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])

```