====== 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

stumpy

最受欢迎

====== STUMPY

如何使用 STUMPY

依赖项

获取方式