|PyPI Version| |Conda Forge Version| |PyPI Downloads| |License| |Test Status| |Code Coverage|

|RTD Status| |Binder| |JOSS| |NumFOCUS|

.. |PyPI Version| image:: https://img.shields.io/pypi/v/stumpy.svg :target: https://pypi.org/project/stumpy/ :alt: PyPI Version .. |Conda Forge Version| image:: https://anaconda.org/conda-forge/stumpy/badges/version.svg :target: https://anaconda.org/conda-forge/stumpy :alt: Conda-Forge Version .. |PyPI Downloads| image:: https://static.pepy.tech/badge/stumpy/month :target: https://pepy.tech/project/stumpy :alt: PyPI Downloads .. |License| image:: https://img.shields.io/pypi/l/stumpy.svg :target: https://github.com/stumpy-dev/stumpy/blob/main/LICENSE.txt :alt: License .. |Test Status| image:: https://github.com/stumpy-dev/stumpy/workflows/Tests/badge.svg :target: https://github.com/stumpy-dev/stumpy/actions?query=workflow%3ATests+branch%3Amain :alt: Test Status .. |Code Coverage| image:: https://img.shields.io/badge/Coverage-100%25-green :alt: Code Coverage .. |RTD Status| image:: https://readthedocs.org/projects/stumpy/badge/?version=latest :target: https://stumpy.readthedocs.io/ :alt: ReadTheDocs Status .. |Binder| image:: https://mybinder.org/badge_logo.svg :target: https://mybinder.org/v2/gh/stumpy-dev/stumpy/main?filepath=notebooks :alt: Binder .. |JOSS| image:: http://joss.theoj.org/papers/10.21105/joss.01504/status.svg :target: https://doi.org/10.21105/joss.01504 :alt: JOSS .. |DOI| image:: https://zenodo.org/badge/184809315.svg :target: https://zenodo.org/badge/latestdoi/184809315 :alt: DOI .. |NumFOCUS| image:: https://img.shields.io/badge/NumFOCUS-Affiliated%20Project-orange.svg?style=flat&colorA=E1523D&colorB=007D8A :target: https://numfocus.org/sponsored-projects/affiliated-projects :alt: NumFOCUS Affiliated Project .. |Twitter| image:: https://img.shields.io/twitter/follow/stumpy_dev.svg?style=social :target: https://twitter.com/stumpy_dev :alt: Twitter

|

.. image:: https://raw.githubusercontent.com/stumpy-dev/stumpy/main/docs/images/stumpy_logo_small.png :target: https://github.com/stumpy-dev/stumpy :alt: STUMPY Logo

====== STUMPY

STUMPY 是一个功能强大且可扩展的 Python 库，它能够高效地计算所谓的 矩阵轮廓 <https://stumpy.readthedocs.io/en/latest/Tutorial_The_Matrix_Profile.html>__，这是一种学术化的说法，意思是“对于时间序列中的每一个（绿色）子序列，自动识别其对应的最近邻（灰色）”：

.. image:: https://github.com/stumpy-dev/stumpy/blob/main/docs/images/stumpy_demo.gif?raw=true :alt: STUMPY Animated GIF

重要的是，一旦你计算出矩阵轮廓（如上图中部面板所示），它就可以用于各种时间序列数据挖掘任务，例如：

• 模式/基序（即较长时间序列中近似重复的子序列）发现
• 异常/新颖性（discord）发现
• shapelet 发现
• 语义分割
• 流式（在线）数据
• 快速近似矩阵轮廓
• 时间序列链（按时间排序的子序列模式集合）
• 用于归纳长时间序列的 snippets
• pan matrix profiles，用于选择最佳子序列窗口大小
• 以及更多 ... <https://www.cs.ucr.edu/~eamonn/100_Time_Series_Data_Mining_Questions__with_Answers.pdf>__

无论你是学者、数据科学家、软件开发人员还是时间序列爱好者，STUMPY 都易于安装，我们的目标是让你更快地获得时间序列洞察。更多信息请参阅 文档 <https://stumpy.readthedocs.io/en/latest/>__。

如何使用 STUMPY

请参阅我们的 API 文档 <https://stumpy.readthedocs.io/en/latest/api.html>__ 获取完整可用函数列表，并查看内容丰富的 教程 <https://stumpy.readthedocs.io/en/latest/tutorials.html>__ 以了解更全面的示例用例。下面你会找到快速演示如何使用 STUMPY 的代码片段。

典型用法（一维时间序列数据），使用 STUMP <https://stumpy.readthedocs.io/en/latest/api.html#stumpy.stump>__：

.. code:: python

import stumpy
import numpy as np

if __name__ == "__main__":
    your_time_series = np.random.rand(10000)
    window_size = 50  # Approximately, how many data points might be found in a pattern 

    matrix_profile = stumpy.stump(your_time_series, m=window_size)

通过 STUMPED <https://stumpy.readthedocs.io/en/latest/api.html#stumpy.stumped>__ 使用 Dask Distributed 对一维时间序列数据进行分布式处理：

.. code:: python

import stumpy
import numpy as np
from dask.distributed import Client

if __name__ == "__main__":
    with Client() as dask_client:
        your_time_series = np.random.rand(10000)
        window_size = 50  # Approximately, how many data points might be found in a pattern 

        matrix_profile = stumpy.stumped(dask_client, your_time_series, m=window_size)

使用 GPU-STUMP <https://stumpy.readthedocs.io/en/latest/api.html#stumpy.gpu_stump>__ 对一维时间序列数据进行 GPU 处理：

.. code:: python

import stumpy
import numpy as np
from numba import cuda

if __name__ == "__main__":
    your_time_series = np.random.rand(10000)
    window_size = 50  # Approximately, how many data points might be found in a pattern
    all_gpu_devices = [device.id for device in cuda.list_devices()]  # Get a list of all available GPU devices

    matrix_profile = stumpy.gpu_stump(your_time_series, m=window_size, device_id=all_gpu_devices)

多维时间序列数据，使用 MSTUMP <https://stumpy.readthedocs.io/en/latest/api.html#stumpy.mstump>__：

.. code:: python

import stumpy
import numpy as np

if __name__ == "__main__":
    your_time_series = np.random.rand(3, 1000)  # Each row represents data from a different dimension while each column represents data from the same dimension
    window_size = 50  # Approximately, how many data points might be found in a pattern

    matrix_profile, matrix_profile_indices = stumpy.mstump(your_time_series, m=window_size)

使用 Dask Distributed MSTUMPED <https://stumpy.readthedocs.io/en/latest/api.html#stumpy.mstumped>__ 进行分布式多维时间序列数据分析：

.. code:: python

import stumpy
import numpy as np
from dask.distributed import Client

if __name__ == "__main__":
    with Client() as dask_client:
        your_time_series = np.random.rand(3, 1000)   # Each row represents data from a different dimension while each column represents data from the same dimension
        window_size = 50  # Approximately, how many data points might be found in a pattern

        matrix_profile, matrix_profile_indices = stumpy.mstumped(dask_client, your_time_series, m=window_size)

时间序列链，使用 Anchored Time Series Chains (ATSC) <https://stumpy.readthedocs.io/en/latest/api.html#stumpy.atsc>__：

.. code:: python

import stumpy
import numpy as np

if __name__ == "__main__":
    your_time_series = np.random.rand(10000)
    window_size = 50  # Approximately, how many data points might be found in a pattern 
    
    matrix_profile = stumpy.stump(your_time_series, m=window_size)

    left_matrix_profile_index = matrix_profile[:, 2]
    right_matrix_profile_index = matrix_profile[:, 3]
    idx = 10  # Subsequence index for which to retrieve the anchored time series chain for

    anchored_chain = stumpy.atsc(left_matrix_profile_index, right_matrix_profile_index, idx)

    all_chain_set, longest_unanchored_chain = stumpy.allc(left_matrix_profile_index, right_matrix_profile_index)

语义分割，使用 Fast Low-cost Unipotent Semantic Segmentation (FLUSS) <https://stumpy.readthedocs.io/en/latest/api.html#stumpy.fluss>__：

.. code:: python

import stumpy
import numpy as np

if __name__ == "__main__":
    your_time_series = np.random.rand(10000)
    window_size = 50  # Approximately, how many data points might be found in a pattern

    matrix_profile = stumpy.stump(your_time_series, m=window_size)

    subseq_len = 50
    correct_arc_curve, regime_locations = stumpy.fluss(matrix_profile[:, 1], 
                                                    L=subseq_len, 
                                                    n_regimes=2, 
                                                    excl_factor=1
                                                    )

依赖项

支持的 Python 和 NumPy 版本根据 NEP 29 弃用策略 <https://numpy.org/neps/nep-0029-deprecation_policy.html>__ 确定。

• NumPy <http://www.numpy.org/>__
• Numba <http://numba.pydata.org/>__
• SciPy <https://www.scipy.org/>__

获取方式

conda:

.. code:: bash

conda install -c conda-forge stumpy

pip:

.. code:: bash

python -m pip install stumpy

pixi:

.. code:: bash

pixi add stumpy

uv:

.. code:: bash

uv add stumpy

要从源码安装 stumpy，请参阅 文档 <https://stumpy.readthedocs.io/en/latest/install.html>__ 中的说明。

文档

为了充分理解和领会底层算法与应用，你务必阅读原始 publications_。如需更详细的使用 STUMPY 示例，请查阅最新 文档 <https://stumpy.readthedocs.io/en/latest/>__ 或探索我们的 实践教程 <https://stumpy.readthedocs.io/en/latest/tutorials.html>__。

性能

我们使用 Numba JIT 编译版本的代码，在具有不同长度的随机生成时间序列数据（即 np.random.rand(n)）以及不同的 CPU 和 GPU 硬件资源 <hardware_>_ 上测试了计算精确矩阵轮廓的性能。

.. image:: https://raw.githubusercontent.com/stumpy-dev/stumpy/main/docs/images/performance.png :alt: STUMPY Performance Plot

原始结果如下表所示，格式为 小时:分钟:秒.毫秒，并且窗口大小固定为 m = 50。请注意，这些报告的运行时间包括将数据从主机传输到所有 GPU 设备所需的时间。你可能需要将表格滚动到右侧才能看到所有运行时间。

+----------+-------------------+--------------+-------------+-------------+-------------+-------------+-------------+-------------+----------------+----------------+ | i | n = 2\ :sup:i | GPU-STOMP | STUMP.2 | STUMP.16 | STUMPED.128 | STUMPED.256 | GPU-STUMP.1 | GPU-STUMP.2 | GPU-STUMP.DGX1 | GPU-STUMP.DGX2 | +==========+===================+==============+=============+=============+=============+=============+=============+=============+================+================+ | 6 | 64 | 00:00:10.00 | 00:00:00.00 | 00:00:00.00 | 00:00:05.77 | 00:00:06.08 | 00:00:00.03 | 00:00:01.63 | NaN | NaN | +----------+-------------------+--------------+-------------+-------------+-------------+-------------+-------------+-------------+----------------+----------------+ | 7 | 128 | 00:00:10.00 | 00:00:00.00 | 00:00:00.00 | 00:00:05.93 | 00:00:07.29 | 00:00:00.04 | 00:00:01.66 | NaN | NaN | +----------+-------------------+--------------+-------------+-------------+-------------+-------------+-------------+-------------+----------------+----------------+ | 8 | 256 | 00:00:10.00 | 00:00:00.00 | 00:00:00.01 | 00:00:05.95 | 00:00:07.59 | 00:00:00.08 | 00:00:01.69 | 00:00:06.68 | 00:00:25.68 | +----------+-------------------+--------------+-------------+-------------+-------------+-------------+-------------+-------------+----------------+----------------+ | 9 | 512 | 00:00:10.00 | 00:00:00.00 | 00:00:00.02 | 00:00:05.97 | 00:00:07.47 | 00:00:00.13 | 00:00:01.66 | 00:00:06.59 | 00:00:27.66 | +----------+-------------------+--------------+-------------+-------------+-------------+-------------+-------------+-------------+----------------+----------------+ | 10 | 1024 | 00:00:10.00 | 00:00:00.02 | 00:00:00.04 | 00:00:05.69 | 00:00:07.64 | 00:00:00.24 | 00:00:01.72 | 00:00:06.70 | 00:00:30.49 | +----------+-------------------+--------------+-------------+-------------+-------------+-------------+-------------+-------------+----------------+----------------+ | 11 | 2048 | NaN | 00:00:00.05 | 00:00:00.09 | 00:00:05.60 | 00:00:07.83 | 00:00:00.53 | 00:00:01.88 | 00:00:06.87 | 00:00:31.09 | +----------+-------------------+--------------+-------------+-------------+-------------+-------------+-------------+-------------+----------------+----------------+ | 12 | 4096 | NaN | 00:00:00.22 | 00:00:00.19 | 00:00:06.26 | 00:00:07.90 | 00:00:01.04 | 00:00:02.19 | 00:00:06.91 | 00:00:33.93 | +----------+-------------------+--------------+-------------+-------------+-------------+-------------+-------------+-------------+----------------+----------------+ | 13 | 8192 | NaN | 00:00:00.50 | 00:00:00.41 | 00:00:06.29 | 00:00:07.73 | 00:00:01.97 | 00:00:02.49 | 00:00:06.61 | 00:00:33.81 | +----------+-------------------+--------------+-------------+-------------+-------------+-------------+-------------+-------------+----------------+----------------+ | 14 | 16384 | NaN | 00:00:01.79 | 00:00:00.99 | 00:00:06.24 | 00:00:08.18 | 00:00:03.69 | 00:00:03.29 | 00:00:07.36 | 00:00:35.23 | +----------+-------------------+--------------+-------------+-------------+-------------+-------------+-------------+-------------+----------------+----------------+ | 15 | 32768 | NaN | 00:00:06.17 | 00:00:02.39 | 00:00:06.48 | 00:00:08.29 | 00:00:07.45 | 00:00:04.93 | 00:00:07.02 | 00:00:36.09 | +----------+-------------------+--------------+-------------+-------------+-------------+-------------+-------------+-------------+----------------+----------------+ | 16 | 65536 | NaN | 00:00:22.94 | 00:00:06.42 | 00:00:07.33 | 00:00:09.01 | 00:00:14.89 | 00:00:08.12 | 00:00:08.10 | 00:00:36.54 | +----------+-------------------+--------------+-------------+-------------+-------------+-------------+-------------+-------------+----------------+----------------+ | 17 | 131072 | 00:00:10.00 | 00:01:29.27 | 00:00:19.52 | 00:00:09.75 | 00:00:10.53 | 00:00:29.97 | 00:00:15.42 | 00:00:09.45 | 00:00:37.33 | +----------+-------------------+--------------+-------------+-------------+-------------+-------------+-------------+-------------+----------------+----------------+ | 18 | 262144 | 00:00:18.00 | 00:05:56.50 | 00:01:08.44 | 00:00:33.38 | 00:00:24.07 | 00:00:59.62 | 00:00:27.41 | 00:00:13.18 | 00:00:39.30 | +----------+-------------------+--------------+-------------+-------------+-------------+-------------+-------------+-------------+----------------+----------------+ | 19 | 524288 | 00:00:46.00 | 00:25:34.58 | 00:03:56.82 | 00:01:35.27 | 00:03:43.66 | 00:01:56.67 | 00:00:54.05 | 00:00:19.65 | 00:00:41.45 | +----------+-------------------+--------------+-------------+-------------+-------------+-------------+-------------+-------------+----------------+----------------+ | 20 | 1048576 | 00:02:30.00 | 01:51:13.43 | 00:19:54.75 | 00:04:37.15 | 00:03:01.16 | 00:05:06.48 | 00:02:24.73 | 00:00:32.95 | 00:00:46.14 | +----------+-------------------+--------------+-------------+-------------+-------------+-------------+-------------+-------------+----------------+----------------+ | 21 | 2097152 | 00:09:15.00 | 09:25:47.64 | 03:05:07.64 | 00:13:36.51 | 00:08:47.47 | 00:20:27.94 | 00:09:41.43 | 00:01:06.51 | 00:01:02.67 | +----------+-------------------+--------------+-------------+-------------+-------------+-------------+-------------+-------------+----------------+----------------+ | 22 | 4194304 | NaN | 36:12:23.74 | 10:37:51.21 | 00:55:44.43 | 00:32:06.70 | 01:21:12.33 | 00:38:30.86 | 00:04:03.26 | 00:02:23.47 | +----------+-------------------+--------------+-------------+-------------+-------------+-------------+-------------+-------------+----------------+----------------+ | 23 | 8388608 | NaN | 143:16:09.94| 38:42:51.42 | 03:33:30.53 | 02:00:49.37 | 05:11:44.45 | 02:33:14.60 | 00:15:46.26 | 00:08:03.76 | +----------+-------------------+--------------+-------------+-------------+-------------+-------------+-------------+-------------+----------------+----------------+ | 24 | 16777216 | NaN | NaN | NaN | 14:39:11.99 | 07:13:47.12 | 20:43:03.80 | 09:48:43.42 | 01:00:24.06 | 00:29:07.84 | +----------+-------------------+--------------+-------------+-------------+-------------+-------------+-------------+-------------+----------------+----------------+ | NaN | 17729800 | 09:16:12.00 | NaN | NaN | 15:31:31.75 | 07:18:42.54 | 23:09:22.43 | 10:54:08.64 | 01:07:35.39 | 00:32:51.55 | +----------+-------------------+--------------+-------------+-------------+-------------+-------------+-------------+-------------+----------------+----------------+ | 25 | 33554432 | NaN | NaN | NaN | 56:03:46.81 | 26:27:41.29 | 83:29:21.06 | 39:17:43.82 | 03:59:32.79 | 01:54:56.52 | +----------+-------------------+--------------+-------------+-------------+-------------+-------------+-------------+-------------+----------------+----------------+ | 26 | 67108864 | NaN | NaN | NaN | 211:17:37.60| 106:40:17.17| 328:58:04.68| 157:18:30.50| 15:42:15.94 | 07:18:52.91 | +----------+-------------------+--------------+-------------+-------------+-------------+-------------+-------------+-------------+----------------+----------------+ | NaN | 100000000 | 291:07:12.00 | NaN | NaN | NaN | 234:51:35.39| NaN | NaN | 35:03:44.61 | 16:22:40.81 | +----------+-------------------+--------------+-------------+-------------+-------------+-------------+-------------+-------------+----------------+----------------+ | 27 | 134217728 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 64:41:55.09 | 29:13:48.12 | +----------+-------------------+--------------+-------------+-------------+-------------+-------------+-------------+-------------+----------------+----------------+

^^^^^^^^^^^^^^^^^^ 硬件资源 ^^^^^^^^^^^^^^^^^^

.. _hardware:

GPU-STOMP：这些结果复制自原始 Matrix Profile II <https://ieeexplore.ieee.org/abstract/document/7837898>__ 论文——NVIDIA Tesla K80（包含 2 个 GPU），用作对比的性能基准。

STUMP.2：stumpy.stump <https://stumpy.readthedocs.io/en/latest/api.html#stumpy.stump>__ 共使用 2 个 CPU 执行——在单台服务器上使用 2x Intel(R) Xeon(R) CPU E5-2650 v4 @ 2.20GHz 处理器，通过 Numba 并行化，未使用 Dask。

STUMP.16：stumpy.stump <https://stumpy.readthedocs.io/en/latest/api.html#stumpy.stump>__ 共使用 16 个 CPU 执行——在单台服务器上使用 16x Intel(R) Xeon(R) CPU E5-2650 v4 @ 2.20GHz 处理器，通过 Numba 并行化，未使用 Dask。STUMPED.128: stumpy.stumped <https://stumpy.readthedocs.io/en/latest/api.html#stumpy.stumped>__ 使用总计 128 个 CPU 执行——16 台服务器，每台配备 8 个 Intel(R) Xeon(R) CPU E5-2650 v4 @ 2.20GHz 处理器，通过 Numba 并行化，并使用 Dask Distributed 进行分布式计算。

STUMPED.256: stumpy.stumped <https://stumpy.readthedocs.io/en/latest/api.html#stumpy.stumped>__ 使用总计 256 个 CPU 执行——32 台服务器，每台配备 8 个 Intel(R) Xeon(R) CPU E5-2650 v4 @ 2.20GHz 处理器，通过 Numba 并行化，并使用 Dask Distributed 进行分布式计算。

GPU-STUMP.1: stumpy.gpu_stump <https://stumpy.readthedocs.io/en/latest/api.html#stumpy.gpu_stump>__ 使用 1 块 NVIDIA GeForce GTX 1080 Ti GPU 执行，每个线程块 512 个线程，200W 功耗限制，使用 Numba 编译为 CUDA，并通过 Python multiprocessing 并行化。

GPU-STUMP.2: stumpy.gpu_stump <https://stumpy.readthedocs.io/en/latest/api.html#stumpy.gpu_stump>__ 使用 2 块 NVIDIA GeForce GTX 1080 Ti GPU 执行，每个线程块 512 个线程，200W 功耗限制，使用 Numba 编译为 CUDA，并通过 Python multiprocessing 并行化。

GPU-STUMP.DGX1: stumpy.gpu_stump <https://stumpy.readthedocs.io/en/latest/api.html#stumpy.gpu_stump>__ 使用 8 块 NVIDIA Tesla V100 执行，每个线程块 512 个线程，使用 Numba 编译为 CUDA，并通过 Python multiprocessing 并行化。

GPU-STUMP.DGX2: stumpy.gpu_stump <https://stumpy.readthedocs.io/en/latest/api.html#stumpy.gpu_stump>__ 使用 16 块 NVIDIA Tesla V100 执行，每个线程块 512 个线程，使用 Numba 编译为 CUDA，并通过 Python multiprocessing 并行化。

运行测试

测试代码位于 tests 目录中，使用 PyTest <https://docs.pytest.org/en/latest/>__ 运行，并需要 coverage.py 进行代码覆盖率分析。可通过以下命令执行测试：

.. code:: bash

./test.sh

Python 版本

STUMPY 支持 Python 3.10+ <https://python3statement.org/>__，并且由于使用了 Unicode 变量名/标识符，因此不兼容 Python 2.x。鉴于依赖项较少，STUMPY 可能可以在更旧的 Python 版本上运行，但这超出了我们的支持范围，我们强烈建议您升级到最新版本的 Python。

获取帮助

首先，请查看 GitHub 上的 讨论 <https://github.com/stumpy-dev/stumpy/discussions>__ 和 问题 <https://github.com/stumpy-dev/stumpy/issues?utf8=%E2%9C%93&q=>__，确认您的问题是否已得到解答。如果那里没有解决方案，欢迎新建讨论或问题，作者将尽力在合理时间内做出回复。

贡献

我们欢迎任何形式的 贡献 <https://github.com/stumpy-dev/stumpy/blob/main/CONTRIBUTING.md>！我们始终欢迎文档方面的帮助，尤其是扩展教程。要贡献代码，请先 复刻（fork）项目 <https://github.com/stumpy-dev/stumpy/fork>，进行修改，然后提交拉取请求（pull request）。我们将尽最大努力与您一起解决任何问题，并将您的代码合并到主分支中。

引用

如果您在科学出版物中使用了本代码库并希望引用它，请使用 Journal of Open Source Software 文章 <http://joss.theoj.org/papers/10.21105/joss.01504>__。

S.M. Law, (2019). *STUMPY: A Powerful and Scalable Python Library for Time Series Data Mining*. Journal of Open Source Software, 4(39), 1504.

.. code:: bibtex

@article{law2019stumpy,
  author  = {Law, Sean M.},
  title   = {{STUMPY: A Powerful and Scalable Python Library for Time Series Data Mining}},
  journal = {{The Journal of Open Source Software}},
  volume  = {4},
  number  = {39},
  pages   = {1504},
  year    = {2019}
}

参考文献

.. _publications:

Yeh, Chin-Chia Michael, et al. (2016) Matrix Profile I: All Pairs Similarity Joins for Time Series: A Unifying View that Includes Motifs, Discords, and Shapelets. ICDM:1317-1322. 链接 <https://ieeexplore.ieee.org/abstract/document/7837992>__

Zhu, Yan, et al. (2016) Matrix Profile II: Exploiting a Novel Algorithm and GPUs to Break the One Hundred Million Barrier for Time Series Motifs and Joins. ICDM:739-748. 链接 <https://ieeexplore.ieee.org/abstract/document/7837898>__

Yeh, Chin-Chia Michael, et al. (2017) Matrix Profile VI: Meaningful Multidimensional Motif Discovery. ICDM:565-574. 链接 <https://ieeexplore.ieee.org/abstract/document/8215529>__

Zhu, Yan, et al. (2017) Matrix Profile VII: Time Series Chains: A New Primitive for Time Series Data Mining. ICDM:695-704. 链接 <https://ieeexplore.ieee.org/abstract/document/8215542>__

Gharghabi, Shaghayegh, et al. (2017) Matrix Profile VIII: Domain Agnostic Online Semantic Segmentation at Superhuman Performance Levels. ICDM:117-126. 链接 <https://ieeexplore.ieee.org/abstract/document/8215484>__

Zhu, Yan, et al. (2017) Exploiting a Novel Algorithm and GPUs to Break the Ten Quadrillion Pairwise Comparisons Barrier for Time Series Motifs and Joins. KAIS:203-236. 链接 <https://link.springer.com/article/10.1007%2Fs10115-017-1138-x>__

Zhu, Yan, et al. (2018) Matrix Profile XI: SCRIMP++: Time Series Motif Discovery at Interactive Speeds. ICDM:837-846. 链接 <https://ieeexplore.ieee.org/abstract/document/8594908>__

Yeh, Chin-Chia Michael, et al. (2018) Time Series Joins, Motifs, Discords and Shapelets: a Unifying View that Exploits the Matrix Profile. Data Min Knowl Disc:83-123. 链接 <https://link.springer.com/article/10.1007/s10618-017-0519-9>__

Gharghabi, Shaghayegh, et al. (2018) "Matrix Profile XII: MPdist: A Novel Time Series Distance Measure to Allow Data Mining in More Challenging Scenarios." ICDM:965-970. 链接 <https://ieeexplore.ieee.org/abstract/document/8594928>__

Zimmerman, Zachary, et al. (2019) Matrix Profile XIV: Scaling Time Series Motif Discovery with GPUs to Break a Quintillion Pairwise Comparisons a Day and Beyond. SoCC '19:74-86. 链接 <https://dl.acm.org/doi/10.1145/3357223.3362721>__

Akbarinia, Reza, and Betrand Cloez. (2019) Efficient Matrix Profile Computation Using Different Distance Functions. arXiv:1901.05708. 链接 <https://arxiv.org/abs/1901.05708>__

Kamgar, Kaveh, et al. (2019) Matrix Profile XV: Exploiting Time Series Consensus Motifs to Find Structure in Time Series Sets. ICDM:1156-1161. 链接 <https://ieeexplore.ieee.org/abstract/document/8970797>__

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