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This project implements the Time2Graph model[1], which focuses on time series modeling with dynamic shapelets.

Quick Links

Building and Testing

This project is implemented primarily in Python 3.6, with several dependencies listed below. We have tested the whole framework on Ubuntu 16.04.5 LTS with kernel 4.4.0, and it is expected to easily build and run under a regular Unix-like system.


  • Python 3.6. Version 3.6.5 has been tested. Higher versions are expected be compatible with current implementation, while there may be syntax errors or conflicts under python 2.x.

  • DeepWalk We use a modified version of the original implementation of deepwalk to satisfy the support for directed and weighted graphs. The source codes with minor modifications can be found on weighted_deepwalk.

  • PyTorch.

    Version 0.4.1 has been tested. You can find installation instructions here. Note that the GPU support is ENCOURAGED as it greatly boosts training efficiency.

  • XGBoost

    Version 0.80 has been tested. You can find installation instructions here.

  • Other Python modules. Some other Python module dependencies are listed in requirements.txt, which can be easily installed with pip:

    pip install -r requirements.txt

    Although not all dependencies are mentioned in the installation instruction links above, you can find most of the libraries in the package repository of a regular Linux distribution.

Building the Project

Before building the project, we recommend switching the working directory to the project root directory. Assume the project root is at <time2graph_root>, then run command

cd <time2graph_root>

Note that we assume <time2graph_root> as your working directory in all the commands presented in the rest of this documentation. Then make sure that the environment variable PYTHONPATH is properly set, by running the following command (on a Linux distribution):

export PYTHONPATH=`readlink -f ./`

Testing the Project (Reproducibility)

A test script scripts/ is available for reproducibility on the benchmark datasets:

python . -h
usage: . [-h] [--dataset] [--n_splits] [--model_cache] [--shapelet_cache] [--gpu_enable]

optional arguments:
  -h, --help        show this help message and exit
  --dataset         str, one of `ucr-Earthquakes`, `ucr-WormsTwoClass` and `ucr-Strawberry`, 
                    which we have set the optimal parameters after fine-tuning. 
                    (default: `ucr-Earthquakes`)
  --n_splits        int, number of splits in cross-validation. (default: 5)
  --model_cache	    bool, whether to use a pretrained model.(default: False)
  --shapelet_cache  bool, whether to use a pretrained shapelets set.(default: False)
  --gpu_enable      bool, whether to enable GPU usage. (default: False)

To quickly and exactly reproduce the results that reported in the paper, we highly RECOMMEND that set model_cache as True, since there are unavoidable randomness in the process of shapelets learning and graph embedding. And if only shapelet_cache is True, it will learn a new set of shapelet embeddings, which may bring some small fluctuations on the performance. So the easiest way for reproducibility and project testing is to run the following command:

python scripts/ --model_cache --dataset *OPTION* --gpu_enable


Given a set of time series data and the corresponding labels, the Time2Graph framework aims to learn the representations of original time series, and conduct time series classifications under the setting of supervised learning.

Input Format

The input time series data and labels are expected to be numpy.ndarray:

Time_Series X: 
    numpy.ndarray with shape (N x L x data_size),
    where N is the number of time series, L is the time series length, 
    and data_size is the data dimension.
Labels Y:
    numpy.ndarray with shape (N x 1), with 0 as negative, and 1 as positive samples.

We organize the preprocessing codes that load the UCR dataset in the archive/ repo, and if you want to utilize the framework on other datasets, just preprocess the original data as the abovementioned format. Note that the time series data is not needed to be normalized or scaled, since you can set the parameter scaled as True when initializing Time2Graph model.

Main Script

Now that the input data is ready, the main script scripts/ is a pipeline example to train and test the whole framework. Firstly you need to modify the codes in the following block (line 46-51) to load your datasets, by reassigning x_train, y_train, x_test, y_test respectively.

if args.dataset.startswith('ucr'):
    dataset = args.dataset.rstrip('\n\r').split('-')[-1]
    x_train, y_train, x_test, y_test = load_usr_dataset_by_name(
    fname=dataset, length=args.seg_length * args.num_segment)
    raise NotImplementedError()

The help information of the main script scripts/ is listed as follows:

python . -h
usage: .[-h] [-- dataset] [--K] [--C] [--num_segment] [--seg_length] [--data_size] 
        [--n_splits] [--njobs] [--optimizer] [--alpha]  [--beta] [--init] 
        [--gpu_enable] [--opt_metric] [--cache] [--embed] [--embed_size] [--warp] 
        [--cmethod] [--kernel] [--percentile] [--measurement] [--batch_size] 
        [--tflag] [--scaled] [--norm] [--no_global]

optional arguments:
  -h, --help        show this help message and exit
  --dataset         str, indicate which dataset to load; 
                    need to modify the codes in line 46-51.
  --K               int, number of shapelets that try to learn
  --C               int, number of shapelet candidates used for learning shapelets
  --num_segment     int, number of segment that a time series have
  --seg_length      int, the segment length, 
                    so the length of a time series is num_segment * seg_length
  --data_size       int, the dimension of time series data
  --n_splits        int, number of cross-validation, default 5.
  --njobs           int, number of threads if using multiprocessing.
  --optimizer       str, optimizer used for learning shapelets, default `Adam`.
  --alpha           float, penalty for local timing factor, default 0.1.
  --beta            float, penalty for global timing factor, default 0.05.
  --init            int, init offset for time series, default 0.
  --gpu_enable      bool, whether to use GPU, default False.
  --opt_metric      str, metric for optimizing out-classifier, default `accuracy`.
  --cache           bool, whether to save model cache, defualt False.
  --embed           str, embedding mode, one of `aggregate` and `concate`.
  --embed_size      int, embedding size in deepwalk, default 256.
  --wrap            int, warp size in greedy-dtw, default 2.
  --cmethod         str, candidate generation method, one of `cluster` and `greedy`
  --kernel          str, choice of outer-classifer, default `xgb`.
  --percentile      int, distance threshold (percentile) in graph construction, default 10
  --measurement     str, distance measurement,default `gdtw`.
  --batch_size      int, batch size, default 50
  --tflag           bool, whether to use timing factors, default True.
  --scaled          bool, whether to scale time seriee by z-normalize, default False.
  --norm            bool, whether to normalize handcraft-features, default False.
  --no_global       bool, whether to use global timing factor 
                    when constructing shapelet evolution graph, default False.

Some of the arguments may require further explanation:

  • --K/--C: the number of shapelets should be carefully selected, and it is highly related with intrinsic properties of the dataset. And in our extensive experiments, C is often set 10 or 20 times of K to ensure that we can learn from a large pool of candidates.
  • --percentile , --alpha and --beta: we have conduct fine-tuning on several datasets, and in most cases we recommend the default settings, although modifying them may bring performance increment, as well as drop.


We include all three benchmark UCR datasets in the dataset directory, which is a subset of UCR-Archive time series dataset. See Data Sets for more details. Then a demo script is available by calling scripts/, as the following:

python scripts/ --dataset ucr-Earthquakes --K 50 --C 500 
--num_segment 21 --seg_length 24 --data_size 1 --embed concate --percentile 5 --gpu_enable


Data Sets

The three benchmark datasets reported in [1] was made public by UCR, which consists of many time series datasets. we select several UCR datasets from many candidates by the following reasons that: 1) to maintain the consistency of evaluation metrics between the real-world and public datasets, we only consider binary-label ones in UCR; 2) we have to make sure that there are enough training cases because we need sufficient samples to capture the normal transitions between shapelets (many binary-label datasets in UCR only have less than 100 training samples), and 3) we omit all datasets categorized as “image”, because the proposed intuition (timing factor, shapelet evolutions) may not be appropriate for time series transformed from images. After filtering based on the abovementioned criterion, and due to space limitation, we only present those three in [1]. We have tested some others such as Ham and Computers, etc., and also achieved competitive results compared with baseline methods.

Furthermore, we apply the proposed Time2Graph model on two real-world scenarios: Electricity Consumption Records (ECR) provided by State Grid of China, and Network Traffic Flow (NTF) from China Telecom. Detailed dataset descriptions can be found in our paper. The performance increment compared with existing models clearly demonstrate the effectiveness of the framework, and below we list the final results along with several popular baselines.


Accuracy on UCR(%) Earthquakes WormsTwoClass Strawberry
NN-DTW 70.31 68.16 95.53
TSF 74.67 68.51 96.27
FS 74.66 70.58 91.66
Time2Graph 79.14 72.73 96.76
Performance on ECR(%) Precision Recall F1
NN-DTW 15.52 18.15 16.73
TSF 26.32 2.02 3.75
FS 10.45 79.84* 18.48
Time2Graph 30.10 40.26 34.44
Performance on NTF(%) Precision Recall F1
NN-DTW 33.20 43.75 37.75
TSF 57.52 33.85 42.62
FS 63.55 35.42 45.49
Time2Graph 71.52 56.25 62.97

Please refer to our paper [1] for detailed information about the experimental settings, the description of unpublished data sets, the full results of our experiments, along with ablation and observational studies.


[1] Cheng, Z; Yang, Y; Wang, W; Hu, W; Zhuang, Y and Song, G, 2020, Time2Graph: Revisiting Time Series Modeling with Dynamic Shapelets, In AAAI, 2020

  title = "{Time2Graph: Revisiting Time Series Modeling with Dynamic Shapelets}", 
  author = {{Cheng}, Z. and {Yang}, Y. and {Wang}, W. and {Hu}, W. and {Zhuang}, Y. and {Song}, G.}, 
  booktitle={Proceedings of Association for the Advancement of Artificial Intelligence (AAAI)},
  year = 2020, 


Source codes for Time2Graph model.






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