Time Series 简明教程
Time Series - Python Libraries
由于其易于编写和易于理解的代码结构以及各种开源库，Python 在从事机器学习的人员中享有很高的知名度。我们在接下来章节中将使用的几个这样的开源库已在下面介绍。
Python has an established popularity among individuals who perform machine learning because of its easy-to-write and easy-to-understand code structure as well as a wide variety of open source libraries. A few of such open source libraries that we will be using in the coming chapters have been introduced below.
NumPy
NumPy（Numerical Python）是一个用于科学计算的库。它处理一个 N 维数组对象，并提供基本的数学功能，如大小、形状、平均值、标准差、最小值、最大值以及更复杂的一些函数，如线性代数函数和傅里叶变换。随着我们在此教程中不断前进，您将了解更多相关内容。
Numerical Python is a library used for scientific computing. It works on an N-dimensional array object and provides basic mathematical functionality such as size, shape, mean, standard deviation, minimum, maximum as well as some more complex functions such as linear algebraic functions and Fourier transform. You will learn more about these as we move ahead in this tutorial.
Pandas
此库提供诸如系列、数据框和面板等高效且易于使用的数据结构。它提升了 Python 的功能，使其从单纯的数据收集和准备转变为数据分析。Pandas 和 NumPy 这两个库大大简化了针对从小到非常大的数据集的任何操作。想要进一步了解这些函数，请查看本教程。
This library provides highly efficient and easy-to-use data structures such as series, dataframes and panels. It has enhanced Python’s functionality from mere data collection and preparation to data analysis. The two libraries, Pandas and NumPy, make any operation on small to very large dataset very simple. To know more about these functions, follow this tutorial.
SciPy
SciPy（Science Python）是一个用于科学和技术计算的库。它提供函数优化、信号和图像处理、积分、插值和线性代数功能。在执行机器学习时，此库非常有用。我们将在此教程中逐步讨论这些功能。
Science Python is a library used for scientific and technical computing. It provides functionalities for optimization, signal and image processing, integration, interpolation and linear algebra. This library comes handy while performing machine learning. We will discuss these functionalities as we move ahead in this tutorial.
Scikit Learn
此库是一个 SciPy 工具包，广泛用于统计建模、机器学习和深度学习，因为它包含各种可定制的回归、分类和聚类模型。它可以很好地与 Numpy、Pandas 和其他库配合使用，从而更易于使用。
This library is a SciPy Toolkit widely used for statistical modelling, machine learning and deep learning, as it contains various customizable regression, classification and clustering models. It works well with Numpy, Pandas and other libraries which makes it easier to use.
Statsmodels
此库与 Scikit Learn 一样，用于统计数据探索和统计建模。它也可与其他 Python 库兼容。
Like Scikit Learn, this library is used for statistical data exploration and statistical modelling. It also operates well with other Python libraries.
Matplotlib
此库适用于各种格式的数据可视化，例如折线图、条形图、热力图、散点图、柱状图，等等。它包含所有所需的图表相关功能，从绘图到标记。我们将在本教程中继续讲解这些功能。
This library is used for data visualization in various formats such as line plot, bar graph, heat maps, scatter plots, histogram etc. It contains all the graph related functionalities required from plotting to labelling. We will discuss these functionalities as we move ahead in this tutorial.
在使用任何类型数据的机器学习入门中，这些库至关重要。
These libraries are very essential to start with machine learning with any sort of data.
除了上述内容，另一个与时间序列极其相关的库是 −
Beside the ones discussed above, another library especially significant to deal with time series is −