Question

我们假设我们有一个时间序列对象，称为＆＃34; series＆＃34; 。我知道很容易使用 autocorrelation_plot（）方法来绘制系列对象的滞后和自相关维度。

以下是代码：

from matplotlib import pyplot
from pandas.tools.plotting import autocorrelation_plot
autocorrelation_plot(series)
pyplot.show()

这是大熊猫的情节：

有没有办法使用散景服务器获得相同的情节？

Answer 1

是的，有。我编写的代码可以为您提供与pandas autocorrelation_plot（）方法相同的结果。

以下是代码：

from bokeh.layouts import column
from bokeh.plotting import figure, curdoc
import timeseries_model_creator  # to get data
import numpy as np

TimeSeriesModelCreator = timeseries_model_creator.TimeSeriesModelCreator()
series = TimeSeriesModelCreator.read_csv()  # time series object

def get_autocorrelation_plot_params(series):
    n = len(series)
    data = np.asarray(series)

    mean = np.mean(data)
    c0 = np.sum((data - mean) ** 2) / float(n)

    def r(h):
        return ((data[:n - h] - mean) *
                (data[h:] - mean)).sum() / float(n) / c0
    x = np.arange(n) + 1
    y = map(r, x)
    print "x : ", x, " y : ", y
    z95 = 1.959963984540054
    z99 = 2.5758293035489004
    return n, x, y, z95, z99

n, x, y, z95, z99 = get_autocorrelation_plot_params(series)

auto_correlation_plot2 = figure(title='Time Series Auto-Correlation', plot_width=1000,
                                plot_height=500, x_axis_label="Lag", y_axis_label="Autocorrelation")

auto_correlation_plot2.line(x, y=z99 / np.sqrt(n), line_dash='dashed', line_color='grey')
auto_correlation_plot2.line(x, y=z95 / np.sqrt(n), line_color='grey')
auto_correlation_plot2.line(x, y=0.0, line_color='black')
auto_correlation_plot2.line(x, y=-z95 / np.sqrt(n), line_color='grey')
auto_correlation_plot2.line(x, y=-z99 / np.sqrt(n), line_dash='dashed', line_color='grey')

auto_correlation_plot2.line(x, y, line_width=2)
auto_correlation_plot2.circle(x, y, fill_color="white", size=8)  # optional

curdoc().add_root(column(auto_correlation_plot2))

这是散景图：

Answer 2

我基本上是在复制艾伯克的答案，但把它变成了一个函数

def acf(series):
    n = len(series)
    data = np.asarray(series)
    mean = np.mean(data)
    c0 = np.sum((data - mean) ** 2) / float(n)
    def r(h):
        acf_lag = ((data[:n - h] - mean) * (data[h:] - mean)).sum() / float(n) / c0
        return round(acf_lag, 3)
    x = np.arange(n) # Avoiding lag 0 calculation
    acf_coeffs = pd.Series(map(r, x)).round(decimals = 3)
    acf_coeffs = acf_coeffs + 0
    return acf_coeffs

def significance(series):
    n = len(series)
    z95 = 1.959963984540054 / np.sqrt(n)
    z99 = 2.5758293035489004 / np.sqrt(n)
    return(z95,z99)

def bok_autocor(series):
    x = pd.Series(range(1, len(series)+1), dtype = float)
    z95, z99 = significance(series)
    y = acf(series)
    p = figure(title='Time Series Auto-Correlation', plot_width=1000,
               plot_height=500, x_axis_label="Lag", y_axis_label="Autocorrelation")
    p.line(x, z99, line_dash='dashed', line_color='grey')
    p.line(x, z95, line_color = 'grey')
    p.line(x, y=0.0, line_color='black')
    p.line(x, z99*-1, line_dash='dashed', line_color='grey')
    p.line(x, z95*-1, line_color = 'grey')
    p.line(x, y, line_width=2)
    return p


show(series.pipe(bok_autocor))

我找到了acf here，它表示它来自pandas autocorrelation_plot函数，并且看起来接近于Ayberk使用的内容。

是否有庞大版本的熊猫自相关绘图方法？

2 个答案: