def test_plot_acf_kwargs(close_figures):
# Just test that it runs.
fig = plt.figure()
ax = fig.add_subplot(111)
ar = np.r_[1., -0.9]
ma = np.r_[1., 0.9]
armaprocess = tsp.ArmaProcess(ar, ma)
rs = np.random.RandomState(1234)
acf = armaprocess.generate_sample(100, distrvs=rs.standard_normal)
buff = BytesIO()
plot_acf(acf, ax=ax)
fig.savefig(buff, format='rgba')
buff_with_vlines = BytesIO()
fig_with_vlines = plt.figure()
ax = fig_with_vlines.add_subplot(111)
vlines_kwargs = {'linestyles': 'dashdot'}
plot_acf(acf, ax=ax, vlines_kwargs=vlines_kwargs)
fig_with_vlines.savefig(buff_with_vlines, format='rgba')
buff.seek(0)
buff_with_vlines.seek(0)
plain = buff.read()
with_vlines = buff_with_vlines.read()
assert_(with_vlines != plain)
def trend_plot_items(request,items):
xdata1 =[]
ydata5 =[]
csvdata = FDataFrame.objects.filter(items=str(items),support_threshold='0.01')
for l in csvdata:
xdata1.append(l.date2str)
ydata5.append(l.confidence)
ydata5 = map(float,ydata5)
xdata1 = map(int,xdata1)
extra_serie = {"tooltip": {"y_start": "There are ", "y_end": " calls"}}
chartdata = {
'x': xdata1,
'name1': 'confidence of Rule: {% rules %}', 'y1': ydata5, 'extra1': extra_serie,
}
charttype = "multiBarChart"
data = {
'charttype': charttype,
'chartdata': chartdata
}
ar = np.r_[1., -0.5, -0.2]; ma = np.r_[1., 0.2, -0.2]
np.random.seed(123)
x = tsp.arma_generate_sample(ar, ma, 20000, burnin=1000)
sm.tsa.pacf(x, 5)
ap = tsp.ArmaProcess(ar, ma)
ap.pacf(5)
return render_to_response('blog/multibarchart.html', data)
def test_plotacf():
# Just test that it runs.
fig = plt.figure()
ax = fig.add_subplot(111)
ar = np.r_[1., -0.9]
ma = np.r_[1., 0.9]
armaprocess = tsp.ArmaProcess(ar, ma)
acf = armaprocess.acf(20)[:20]
plotacf(acf, ax=ax)
plt.close(fig)
def test_plot_pacf(close_figures):
# Just test that it runs.
fig = plt.figure()
ax = fig.add_subplot(111)
ar = np.r_[1., -0.9]
ma = np.r_[1., 0.9]
armaprocess = tsp.ArmaProcess(ar, ma)
rs = np.random.RandomState(1234)
pacf = armaprocess.generate_sample(100, distrvs=rs.standard_normal)
plot_pacf(pacf, ax=ax)
plot_pacf(pacf, ax=ax, alpha=None)
def test_plot_acf_irregular(close_figures):
# Just test that it runs.
fig = plt.figure()
ax = fig.add_subplot(111)
ar = np.r_[1., -0.9]
ma = np.r_[1., 0.9]
armaprocess = tsp.ArmaProcess(ar, ma)
rs = np.random.RandomState(1234)
acf = armaprocess.generate_sample(100, distrvs=rs.standard_normal)
plot_acf(acf, ax=ax, lags=np.arange(1, 11))
plot_acf(acf, ax=ax, lags=10, zero=False)
plot_acf(acf, ax=ax, alpha=None, zero=False)
def test_plot_pacf(close_figures):
# Just test that it runs.
fig = plt.figure()
ax = fig.add_subplot(111)
ar = np.r_[1.0, -0.9]
ma = np.r_[1.0, 0.9]
armaprocess = tsp.ArmaProcess(ar, ma)
rs = np.random.RandomState(1234)
pacf = armaprocess.generate_sample(100, distrvs=rs.standard_normal)
with pytest.warns(FutureWarning):
plot_pacf(pacf, ax=ax)
with pytest.warns(FutureWarning, match="The default"):
plot_pacf(pacf, ax=ax, alpha=None)
def test_plot_pacf_kwargs(close_figures):
# Just test that it runs.
fig = plt.figure()
ax = fig.add_subplot(111)
ar = np.r_[1.0, -0.9]
ma = np.r_[1.0, 0.9]
armaprocess = tsp.ArmaProcess(ar, ma)
rs = np.random.RandomState(1234)
pacf = armaprocess.generate_sample(100, distrvs=rs.standard_normal)
buff = BytesIO()
with pytest.warns(FutureWarning, match="The default"):
plot_pacf(pacf, ax=ax)
fig.savefig(buff, format="rgba")
buff_linestyle = BytesIO()
fig_linestyle = plt.figure()
ax = fig_linestyle.add_subplot(111)
with pytest.warns(FutureWarning, match="The default"):
plot_pacf(pacf, ax=ax, ls="-")
fig_linestyle.savefig(buff_linestyle, format="rgba")
buff_with_vlines = BytesIO()
fig_with_vlines = plt.figure()
ax = fig_with_vlines.add_subplot(111)
vlines_kwargs = {"linestyles": "dashdot"}
with pytest.warns(FutureWarning, match="The default"):
plot_pacf(pacf, ax=ax, vlines_kwargs=vlines_kwargs)
fig_with_vlines.savefig(buff_with_vlines, format="rgba")
buff.seek(0)
buff_linestyle.seek(0)
buff_with_vlines.seek(0)
plain = buff.read()
linestyle = buff_linestyle.read()
with_vlines = buff_with_vlines.read()
assert_(plain != linestyle)
assert_(with_vlines != plain)
assert_(linestyle != with_vlines)
def test_plot_acf_missing(close_figures):
# Just test that it runs.
fig = plt.figure()
ax = fig.add_subplot(111)
ar = np.r_[1., -0.9]
ma = np.r_[1., 0.9]
armaprocess = tsp.ArmaProcess(ar, ma)
rs = np.random.RandomState(1234)
acf = armaprocess.generate_sample(100, distrvs=rs.standard_normal)
acf[::13] = np.nan
buff = BytesIO()
plot_acf(acf, ax=ax, missing='drop')
fig.savefig(buff, format='rgba')
buff.seek(0)
fig = plt.figure()
ax = fig.add_subplot(111)
buff_conservative = BytesIO()
plot_acf(acf, ax=ax, missing='conservative')
fig.savefig(buff_conservative, format='rgba')
buff_conservative.seek(0)
assert_(buff.read() != buff_conservative.read())
fig = plt.figure(figsize=(8, 13))
fig.suptitle(
'ARMA: Autocorrelation (left) and Partial Autocorrelation (right)')
subplotcount = 1
nrows = 4
for arcoef in arcoefs[:-1]:
for macoef in macoefs[:-1]:
ar = np.r_[1., -arcoef]
ma = np.r_[1., macoef]
#from statsmodels.sandbox.tsa.fftarma import ArmaFft as FftArmaProcess
#y = tsp.arma_generate_sample(ar,ma,nsample, sig, burnin)
#armaprocess = FftArmaProcess(ar, ma, nsample) #TODO: make n optional
#armaprocess.plot4()
armaprocess = tsp.ArmaProcess(ar, ma)
acf = armaprocess.acf(20)[:20]
pacf = armaprocess.pacf(20)[:20]
ax = fig.add_subplot(nrows, 2, subplotcount)
plotacf(acf, ax=ax)
## ax.set_title('Autocorrelation \nar=%s, ma=%rs' % (ar, ma),
## size='xx-small')
ax.text(
0.7,
0.6,
'ar =%s \nma=%s' % (ar, ma),
transform=ax.transAxes,
horizontalalignment='left', #'right',
size='xx-small')
ax.set_xlim(-1, 20)
subplotcount += 1
paraAR_mle, paraMA_mle = armaResult.arparams, armaResult.maparams
paraSigma2_mle = armaResult.sigma2
break
print('\n----3.1 (2)----\n')
print(' Coefficents of AR: {0}\n'.format(formatIter(paraAR_mle)))
print(' Coefficents of MA: {0}\n'.format(formatIter(paraMA_mle)))
print(' Sigma^2: %.4f\n' % paraSigma2_mle)
print(' Method：Maximum Likelihood Estimation')
'''
# (3)
use attributes isstationary and isinvertible
of Class arima_process.ArmaProcess
'''
armaProcess = arima_process.ArmaProcess(np.append(1, -paraAR_mle),
np.append(1, paraMA_mle))
stationary = armaProcess.isstationary
invertible = armaProcess.isinvertible
print('\n----3.1 (3)----\n')
print(' Is stationary: {0}'.format(stationary))
print(' Is invertible: {0}'.format(invertible))
'''
## (4)
100 repetition of (2)
100 results are stored in a variable data
so that Mean, Var, MSE of results can be computed
'''
data = np.array([np.append(np.append(paraAR_mle, paraMA_mle), paraSigma2)])
i = 1
#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Tue Jun 18 10:19:06 2019 @author: mariaculjak Running LjungBoxtest.py ; plot_acf_pacf.py Run this command in the terminal while making sure your terminal location is in the same directory as .whl file: pip install PythonTsa-1.2-py3-none-any.whl """ import numpy as np from PythonTsa.LjungBoxtest import * from PythonTsa.plot_acf_pacf import * import statsmodels.tsa.arima_process as arp ar = np.array([1., -0.75, 0.25]) ar_process = arp.ArmaProcess(ar=ar, nobs=1000) sample = ar_process.generate_sample() acf_pacf_fig(x=sample) qstatf(x=sample, type="LjungBox", noestimatedcoef=0, nolags=1) acovf(x=sample, unbiased=False, demean=True, fft=False, missing='none') plot_LB_pvalue(x=sample, noestimatedcoef=0, nolags=1) qstatf(x=sample, noestimatedcoef=0, nolags=1, type="LjungBox")
import statsmodels.tsa.arima_process as sm from statsmodels.graphics.tsaplots import * #4. arma = sm.ArmaProcess([-1, -0.6], [1]) sample = arma.generate_sample(200) plot_acf(sample, lags=20) plot_pacf(sample, lags=20) #5. arma = sm.ArmaProcess([-1], [1, 0.4]) sample = arma.generate_sample(200) plot_acf(sample, lags=20) plot_pacf(sample, lags=20) #6. import statsmodels.tsa.arima_process as sm from statsmodels.graphics.tsaplots import * import numpy as np import pandas as pd numbers = np.random.normal(size=100) numbers = pd.Series(numbers) numbers.plot() plot_acf(numbers, lags=20) from statsmodels.tsa import stattools stattools.arma_order_select_ic(numbers.values, max_ma=4) #7.
#--------------------------------------------------------------------------------
# To choose the number of lagged terms, p and q, for ARIMA(p,d,q) processes, use the
# Box-Jenkins methodology to look at the pattern in the autocorrelation (acf) and
# partial autocorrelation (pacf) functions.
# scikits.statsmodels.tsa.arima_process contains a class that provides several properties
# of ARMA processes and a random process generator. This allows easy comparison of the
# theoretical properties of an ARMA process with their empirical counterparts.
# For exmaple, define the lag coefficients for an ARMA(2,2) process, generate a random
# process and compare the observed and theoretical pacf:
ar = np.r_[1., -0.5, -0.2]
ma = np.r_[1., 0.2, -0.2]
np.random.seed(123)
x = ap.arma_generate_sample(ar, ma, 20000, burnin=1000)
print "observed pacf: {0}".format(sm.tsa.pacf(x, 5))
theo_ap = ap.ArmaProcess(ar, ma)
print " theo pacf: {0}\n".format(theo_ap.pacf(6))
# We can see that observed and theoretical pacf are very close in a large generated
# sample like this.
#--------------------------------------------------------------------------------
# We can use Statsmodels Autoregressive Moving Average (ARMA) time-series models to
# simulate a series:
ar_coef = [1, .75, -.25]
ma_coef = [1, -.5]
nobs = 100
y = ap.arma_generate_sample(ar_coef, ma_coef, nobs)
y += 4 # add in a constant
# Estimate an ARMA model of the series
mod = tsa.ARMA(y, (2, 1))
res = mod.fit(order=(2, 1), trend='c', method='css-mle', disp=-1)
print "Estimated ARMA model params: {0}\n".format(res.params)
