python数据分析学习笔记七

第七章 信号处理与时间序列

（需要统计学知识）

1 statsmodels 子库

示例代码如下

import pkgutil as pu import pydoc import statsmodels as sm # statmodels版本号 print("statmodels version", sm.__version__) def clean(astr):     s = astr     # remove multiple spaces     s = ' '.join(s.split())     s = s.replace('=', '')     return s def print_desc(prefix, pkg_path):     print("pkg_path", pkg_path)     '''     :param prefix: 模块名称     :param pkg_path:模块包路径     :return:     '''     for pkg in pu.iter_modules(path=pkg_path):         '''Yields (module_loader,模块          name,子库名          ispkg)是否'''         print("pkg", pkg)         name = prefix + "." + pkg[1]         # 输出子库名,帮助文档信息         if pkg[2] == True:             try:                 docstr = pydoc.plain(pydoc.render_doc(name))                 docstr = clean(docstr)                 start = docstr.find("DESCRIPTION")                 docstr = docstr[start: start + 140]                 print(name, docstr)             except:                 continue print_desc("statsmodels", sm.__path__)

运行结果如下:

statmodels version 0.8.0rc1

statsmodels.base

statsmodels.compat

statsmodels.datasets

statsmodels.discrete

statsmodels.distributions

statsmodels.duration

statsmodels.emplike

statsmodels.formula

statsmodels.genmod

statsmodels.graphics

statsmodels.imputation

statsmodels.interface

statsmodels.iolib

statsmodels.miscmodels

statsmodels.multivariate

statsmodels.nonparametric DESCRIPTION Foran overview of this module, see docs/source/nonparametric.rst PACKAGE CONTENTS_kernel_base _smoothers_lowess api bandwidths

statsmodels.regression

statsmodels.resampling

statsmodels.robust

statsmodels.sandbox

statsmodels.src

statsmodels.stats

statsmodels.tools

statsmodels.tsa

 

2 移动平均值

示例代码如下:

import matplotlib.pyplot as plt import statsmodels.api as sm from pandas.stats.moments import rolling_mean data_loader = sm.datasets.sunspots.load_pandas() df = data_loader.data year_range = df['YEAR'].values plt.plot(year_range, df["SUNACTIVITY"].values, label="Original") plt.plot(year_range, rolling_mean(df, 11)["SUNACTIVITY"].values, label="SMA 11") plt.plot(year_range, rolling_mean(df, 22)["SUNACTIVITY"].values, label="SMA 22") plt.legend() plt.show()

 

 

 

 

方法二:

import matplotlib.pyplot as plt import statsmodels.api as sm from pandas.stats.moments import rolling_mean import pandas.core.generic data_loader = sm.datasets.sunspots.load_pandas() df = data_loader.data df11 = data_loader.data.rolling(window=11, center=False).mean() df22 = data_loader.data.rolling(window=22, center=False).mean() year_range = df['YEAR'].values plt.plot(year_range, df["SUNACTIVITY"].values, label="Original") # plt.plot(year_range, rolling_mean(df, window=11, center=False)["SUNACTIVITY"].values, label="SMA 11") # plt.plot(year_range, rolling_mean(df, window=22, center=False)["SUNACTIVITY"].values, label="SMA 22") plt.plot(year_range, df11["SUNACTIVITY"].values, label="SMA 11") plt.plot(year_range, df22["SUNACTIVITY"].values, label="SMA 22") plt.legend() plt.show()

 

运行结果如下:

 

3 窗口函数

 是定义在一个区间(窗口)上的函数,超出定义,函数值取零,可以用来分析频谱,设计滤波器

import matplotlib.pyplot as plt import statsmodels.api as sm from pandas.stats.moments import rolling_window import pandas as pd data_loader = sm.datasets.sunspots.load_pandas() # 从尾部取150行 df = data_loader.data.tail(150) df = pd.DataFrame({"SUNACTIVITY": df['SUNACTIVITY'].values}, index=df['YEAR']) ax = df.plot() def plot_window(win_type):     # df2 = rolling_window(df, 22, win_type)     df2 = df.rolling(window=22, win_type=win_type).mean()     df2.columns = [win_type]     # 显示原始数据     df2.plot(ax=ax) # 矩形窗口 plot_window("boxcar") # 三角形窗口 plot_window("triang") # 布莱克曼窗口 plot_window("blackman") # 汉宁窗 plot_window("hanning") # 巴特莱特窗 plot_window("bartlett") # 显示网格 plt.grid() plt.show()

运行结果如下:

4 协整的定义

示例代码如下:

import statsmodels.api as sm from pandas.stats.moments import rolling_window import pandas as pd import statsmodels.tsa.stattools as ts import numpy as np # 用来计算ADF统计量 def calc_adf(x, y):     result = sm.OLS(x, y).fit()     return ts.adfuller(result.resid) # 太阳黑子数据载入到numpy数组 data_loader = sm.datasets.sunspots.load_pandas() data = data_loader.data.values N = len(data) # 计算正弦值,求出该值与自身的协整关系 t = np.linspace(-2 * np.pi, 2 * np.pi, N) sine = np.sin(np.sin(t)) print('Self ADF', calc_adf(sine, sine)) # 给正弦波添加噪音 noise = np.random.normal(0, .01, N) print('ADF sine with noise', calc_adf(sine, sine + noise)) # 生成一个幅值和偏移量更大的余弦波,并混入噪音 cosine = 100 * np.cos(t) + 10 print('ADF sine vs cosine with noise', calc_adf(sine, cosine + noise)) # 正弦与太阳黑子 print('Sine vs sunspots', calc_adf(sine, data))

运行结果如下:

Self ADF (2.1752959320935576e-16,

 0.95853208606005602,

0,

308,

 {'10%': -2.5717944160060719,

'1%': -3.4517611601803702,

'5%': -2.8709700936076912},

 -21598.896016765088)

 

ADF sine with noise (-11.80572756306368,

 9.1062110841151392e-22,

 2,

306,

{'10%': -2.5718274501260199,

'1%': -3.4519023023726696,

 '5%': -2.8710320399170537},

 -1857.1417094083959)

 

ADF sine vs cosine with noise(-6.9222457355201135,

1.1386106445203264e-09,

 16,

292,

{'10%': -2.5720714378870331,

 '1%': -3.4529449243622383,

'5%': -2.8714895534256861},

-10180.957513197414)

 

Sine vs sunspots (-6.7242691810700963,

 3.4210811915549913e-09,

 16,

292,

{'10%': -2.5720714378870331,

'1%': -3.4529449243622383,

'5%': -2.8714895534256861},

-1102.5867415291168)

 

 

5 自相关

数据集内部的相关性,可以用来指明趋势

示例代码如下:

import numpy as np import pandas as pd import statsmodels.api as sm import matplotlib.pyplot as plt from pandas.tools.plotting import autocorrelation_plot # 读入测试数据 data_loader = sm.datasets.sunspots.load_pandas() data = data_loader.data['SUNACTIVITY'].values # 计算自相关值 y = data - np.mean(data) norm = np.sum(y ** 2) correlated = np.correlate(y, y, mode='full') / norm res = correlated[len(correlated) / 2:] # 关联度最高值的索引 print(np.argsort(res)[-5:]) # 结果为[ 9 11 10  1  0] # 绘制图形 plt.plot(res) plt.grid(True) plt.xlabel('Lag') plt.ylabel('Autocorrelation') plt.show() # 使用pandas绘制 autocorrelation_plot(data) plt.show()

 

运行结果如下:

 

使用pandas绘制

6 自回归模型

可用于预测时间序列将来的值

一元线性回归的计算公式(因变量y与自变量x之间的线性关系)

β0和β1为模型的参数

ε误差项一个期望值为0的随机变量

回归方程

E(y)=β0+β1x

 

自加归模型的数学公式

示例代码如下：

from scipy.optimize import leastsq import statsmodels.api as sm import matplotlib.pyplot as plt import numpy as np # 搭建模型代码 def model(p, x1, x10):     p1, p10 = p     return p1 * x1 + p10 * x10 def error(p, data, x1, x10):     return data - model(p, x1, x10) # 给参数表赋值 def fit(data):     p0 = [.5, 0.5]     params = leastsq(error, p0, args=(data[10:], data[9:-1], data[:-10]))[0]     return params # 加载数据 data_loader = sm.datasets.sunspots.load_pandas() sunspots = data_loader.data['SUNACTIVITY'].values print(sunspots) # 训练模型 cutoff = .9 * len(sunspots) params = fit(sunspots[:cutoff]) print('Params', params) # 取得各个指标的值 pred = params[0] * sunspots[cutoff - 1:-1] + params[1] * sunspots[cutoff - 10:-10] actual = sunspots[cutoff:] print('Root mean square error', np.sqrt(np.mean(actual - pred) ** 2)) print('Mean absolute error ', np.mean(np.abs(actual - pred))) print('Mean absolute percentage error', 100 * np.mean(np.abs(actual - pred) / actual)) mid = (actual + pred) / 2 print('Symmetric Mean absolute percentage error', 100 * np.mean(np.abs(actual - pred) / mid)) print('Cofficient of determination', 1 - ((actual - pred) ** 2).sum() / ((actual - actual.mean()) ** 2).sum()) year_range = data_loader.data['YEAR'].values[cutoff:] # 绘制图像 # 太阳黑子活动数值 plt.plot(year_range, actual, 'o', label='Sunspots') # 预测值 plt.plot(year_range, pred, 'x', label='Prediction') plt.grid(True) plt.xlabel('YEAR') plt.ylabel('SUNACTIVITY') plt.legend() plt.show()

 

运行结果如下:

Params [ 0.67172672  0.33626295]

 

Root mean square error 1.02884848439

Mean absolute error  17.6515446503

Mean absolute percentage error60.7817800736

Symmetric Mean absolute percentage error34.9843386176

Cofficient of determination 0.799940292779

 

 

7 ARMA模型

由自回归模型和移动平均模型结合而成,用于时间序列的预测

 

示例代码如下：

import pandas as pd import matplotlib.pyplot as plt import statsmodels.api as sm import datetime # 加载数据 data_loader = sm.datasets.sunspots.load_pandas() df = data_loader.data # 拟合数据 years = df['YEAR'].values.astype(int) str(years[0]) df.index = pd.Index(sm.tsa.datetools.dates_from_range(str(years[0]), str(years[-1]))) del df['YEAR'] # 预测数据 model = sm.tsa.ARMA(df, (10, 1)).fit() prediction = model.predict('1975', str(years[-1]), dynamic=True) # 绘制数据 # 太阳黑子活动数据 df['1975':].plot() # 预测数据 prediction.plot(style='--', label='Prediction') plt.grid(True) plt.legend() plt.show()

 

运行结果如下：

 

 

 

8 生成周期信号

示例代码如下：

from scipy.optimize import leastsq import statsmodels.api as sm import matplotlib.pyplot as plt import numpy as np # 搭建模型 def model(p, t):     C, p1, f1, phi1, p2, f2, phi2, p3, f3, phi3 = p     return C + p1 * np.sin(f1 * t + phi1) + p2 * np.sin(f2 * t + phi2) + p3 * np.sin(f3 * t + phi3) def error(p, y, t):     return y - model(p, t) # 给参数表赋值 def fit(y, t):     p0 = [y.mean(), 0, 2 * np.pi / 11, 0, 0, 2 * np.pi / 22, 0, 0, 2 * np.pi / 100, 0]     params = leastsq(error, p0, args=(y, t))[0]     return params # 加载数据 data_loader = sm.datasets.sunspots.load_pandas() sunspots = data_loader.data["SUNACTIVITY"].values years = data_loader.data["YEAR"].values # 训练模型 cutoff = .9 * len(sunspots) params = fit(sunspots[:cutoff], years[:cutoff]) print("Params", params) # 取得各个指标的值 pred = model(params, years[cutoff:]) actual = sunspots[cutoff:] print("Root mean square error", np.sqrt(np.mean((actual - pred) ** 2))) print("Mean absolute error", np.mean(np.abs(actual - pred))) print("Mean absolute percentage error", 100 * np.mean(np.abs(actual - pred) / actual)) mid = (actual + pred) / 2 print("Symmetric Mean absolute percentage error", 100 * np.mean(np.abs(actual - pred) / mid)) print("Coefficient of determination", 1 - ((actual - pred) ** 2).sum() / ((actual - actual.mean()) ** 2).sum()) year_range = data_loader.data["YEAR"].values[cutoff:] # 绘制图形 plt.plot(year_range, actual, 'o', label="Sunspots") plt.plot(year_range, pred, 'x', label="Prediction") plt.grid(True) plt.xlabel("YEAR") plt.ylabel("SUNACTIVITY") plt.legend() plt.show()

运行结果如下：

Params [ 47.18800156  28.89947466  0.56827281   6.51174621   4.55214731

  0.29372076 -14.3092358 -18.16524066   0.06574835  -4.37789397]

Root mean square error 59.5619988827

Mean absolute error 44.581532168  #平均绝结误差

Mean absolute percentage error65.1643378479

Symmetric Mean absolute percentage error78.4479302694

Coefficient of determination-0.363528934815  #判定系数应尽量接近于1

9 傅量叶分析

FFT (fast fourier transform)快速傅里叶变换

示例代码如下：

import numpy as np import statsmodels.api as sm import matplotlib.pyplot as plt from scipy.fftpack import rfft from scipy.fftpack import fftshift # 加载数据 data_loader = sm.datasets.sunspots.load_pandas() sunspots = data_loader.data["SUNACTIVITY"].values # 创建一个正弦波 t = np.linspace(-2 * np.pi, 2 * np.pi, len(sunspots)) mid = np.ptp(sunspots) / 2 sine = mid + mid * np.sin(np.sin(t)) sine_fft = np.abs(fftshift(rfft(sine))) # 最大振幅的相应索引 print("Index of max sine FFT", np.argsort(sine_fft)[-5:]) # 对太阳黑子进行 fft transformed = np.abs(fftshift(rfft(sunspots))) print("Indices of max sunspots FFT", np.argsort(transformed)[-5:]) # 太阳黑子活动数据和sine函数 plt.subplot(311) plt.plot(sunspots, label="Sunspots") plt.plot(sine, lw=2, label="Sine") plt.grid(True) plt.legend() # 傅里叶变换后的太阳黑子活动数据 plt.subplot(312) plt.plot(transformed, label="Transformed Sunspots") plt.grid(True) plt.legend() # 傅里叶变换后的sine函数 plt.subplot(313) plt.plot(sine_fft, lw=2, label="Transformed Sine") plt.grid(True) plt.legend() plt.show()

运行结果如下：

Index of max sine FFT [160 157 166 158 154]

Indices of max sunspots FFT [205 212 215209 154]

 

10 谱分析

功率频谱

示例代码如下：

import numpy as np import statsmodels.api as sm import matplotlib.pyplot as plt from scipy.fftpack import rfft from scipy.fftpack import fftshift # 加载数据 data_loader = sm.datasets.sunspots.load_pandas() sunspots = data_loader.data["SUNACTIVITY"].values # 创建一个正弦波 t = np.linspace(-2 * np.pi, 2 * np.pi, len(sunspots)) mid = np.ptp(sunspots) / 2 sine = mid + mid * np.sin(np.sin(t)) # 对正弦波进行FFT sine_fft = np.abs(fftshift(rfft(sine))) # 最大振幅的相应索引 print("Index of max sine FFT", np.argsort(sine_fft)[-5:]) # 对太阳黑子进行 fft transformed = fftshift(rfft(sunspots)) print("Indices of max sunspots FFT", np.argsort(transformed)[-5:]) # 太阳黑子活动数据和sine函数 plt.subplot(311) plt.plot(sunspots, label="Sunspots") plt.plot(sine, lw=2, label="Sine") plt.grid(True) plt.legend() # 绘制功率频谱 plt.subplot(312) plt.plot(transformed ** 2, label="Power Spectrum") plt.grid(True) plt.legend() # 相位谱,正弦函数的起始角 plt.subplot(313) plt.plot(np.angle(transformed), label="Phase Spectrum") plt.grid(True) plt.legend() plt.show()

 

运行结果如下：

11 滤波

是一种信号处理技术,可以对信号的某些部分进行删减或抑制，可以对高频或是低频进行滤波

中值滤波

Wiener滤波

Detrend滤波

 

示例代码如下：

import statsmodels.api as sm import matplotlib.pyplot as plt from scipy.signal import medfilt from scipy.signal import wiener from scipy.signal import detrend # 加载数据 data_loader = sm.datasets.sunspots.load_pandas() sunspots = data_loader.data['SUNACTIVITY'].values years = data_loader.data['YEAR'].values # 绘制图像 # 原始数据 plt.plot(years, sunspots, label='SUNATCIVITY') # 中值滤波 plt.plot(years, medfilt(sunspots, 11), lw=2, label='Meadian') # wiener滤波 plt.plot(years, wiener(sunspots, 11), '--', lw=2, label='Wiener') # detrend滤波 plt.plot(years, detrend(sunspots), lw=3, label='Detrend') plt.xlabel('YEAR') plt.grid(True) plt.legend() plt.show()

 

运行结果如下：