#!/usr/bin/env python
# coding: utf-8
#
# *This notebook contains material from [Controlling Natural Watersheds](https://jckantor.github.io/Controlling-Natural-Watersheds);
# content is available [on Github](https://github.com/jckantor/Controlling-Natural-Watersheds.git).*
#
# < [Projects](http://nbviewer.jupyter.org/github/jckantor/Controlling-Natural-Watersheds/blob/master/notebooks/B.00-Projects.ipynb) | [Contents](toc.ipynb) | [Dashboard](http://nbviewer.jupyter.org/github/jckantor/Controlling-Natural-Watersheds/blob/master/notebooks/B.02-Dashboard.ipynb) >
# # Solar Cycle
# In[18]:
get_ipython().run_line_magic('matplotlib', 'inline')
# ARMA example using sunpots data
import numpy as np
from scipy import stats
import pandas
import matplotlib.pyplot as plt
import statsmodels.api as sm
from statsmodels.graphics.api import qqplot
# In[17]:
print(sm.datasets.sunspots.NOTE)
dta = sm.datasets.sunspots.load_pandas().data
dta.index = pandas.Index(sm.tsa.datetools.dates_from_range('1700', '2008'))
del dta["YEAR"]
dta.plot(figsize=(12,8));
# In[16]:
fig = plt.figure(figsize=(12,8))
ax1 = fig.add_subplot(211)
fig = sm.graphics.tsa.plot_acf(dta.values.squeeze(), lags=40, ax=ax1)
ax2 = fig.add_subplot(212)
fig = sm.graphics.tsa.plot_pacf(dta, lags=40, ax=ax2)
# In[8]:
#
arma_mod20 = sm.tsa.ARMA(dta, (2,0)).fit()
print(arma_mod20.params)
print(arma_mod20.aic, arma_mod20.bic, arma_mod20.hqic)
# In[9]:
#
arma_mod30 = sm.tsa.ARMA(dta, (3,0)).fit()
#
print(arma_mod30.params)
#
print(arma_mod30.aic, arma_mod30.bic, arma_mod30.hqic)
# In[12]:
#
# * Does our model obey the theory?
#
sm.stats.durbin_watson(arma_mod30.resid.values)
#
fig = plt.figure(figsize=(12,8))
ax = fig.add_subplot(111)
ax = arma_mod30.resid.plot(ax=ax);
# In[13]:
resid = arma_mod30.resid
stats.normaltest(resid)
# In[11]:
#
fig = plt.figure(figsize=(12,8))
ax = fig.add_subplot(111)
fig = qqplot(resid, line='q', ax=ax, fit=True)
#
fig = plt.figure(figsize=(12,8))
ax1 = fig.add_subplot(211)
fig = sm.graphics.tsa.plot_acf(resid.values.squeeze(), lags=40, ax=ax1)
ax2 = fig.add_subplot(212)
fig = sm.graphics.tsa.plot_pacf(resid, lags=40, ax=ax2)
# In[10]:
#
r,q,p = sm.tsa.acf(resid.values.squeeze(), qstat=True)
data = np.c_[range(1,41), r[1:], q, p]
table = pandas.DataFrame(data, columns=['lag', "AC", "Q", "Prob(>Q)"])
print(table.set_index('lag'))
# In[3]:
#
# * This indicates a lack of fit.
#
# * In-sample dynamic prediction. How good does our model do?
#
predict_sunspots = arma_mod30.predict('1990', '2012', dynamic=True)
print predict_sunspots
#
ax = dta.ix['1950':].plot(figsize=(12,8))
ax = predict_sunspots.plot(ax=ax, style='r--', label='Dynamic Prediction');
ax.legend();
ax.axis((-20.0, 38.0, -4.0, 200.0));
#
def mean_forecast_err(y, yhat):
return y.sub(yhat).mean()
#
mean_forecast_err(dta.SUNACTIVITY, predict_sunspots)
#
# Exercise: Can you obtain a better fit for the Sunspots model? (Hint: sm.tsa.AR has a method select_order)
#
# Simulated ARMA(4,1): Model Identification is Difficult
#
from statsmodels.tsa.arima_process import arma_generate_sample, ArmaProcess
#
np.random.seed(1234)
# include zero-th lag
arparams = np.array([1, .75, -.65, -.55, .9])
maparams = np.array([1, .65])
#
# * Let's make sure this models is estimable.
#
arma_t = ArmaProcess(arparams, maparams)
#
arma_t.isinvertible()
#
arma_t.isstationary()
#
# * What does this mean?
#
fig = plt.figure(figsize=(12,8))
ax = fig.add_subplot(111)
ax.plot(arma_t.generate_sample(size=50));
#
arparams = np.array([1, .35, -.15, .55, .1])
maparams = np.array([1, .65])
arma_t = ArmaProcess(arparams, maparams)
arma_t.isstationary()
#
arma_rvs = arma_t.generate_sample(size=500, burnin=250, scale=2.5)
#
fig = plt.figure(figsize=(12,8))
ax1 = fig.add_subplot(211)
fig = sm.graphics.tsa.plot_acf(arma_rvs, lags=40, ax=ax1)
ax2 = fig.add_subplot(212)
fig = sm.graphics.tsa.plot_pacf(arma_rvs, lags=40, ax=ax2)
#
# * For mixed ARMA processes the Autocorrelation function is a mixture of exponentials and damped sine waves after (q-p) lags.
# * The partial autocorrelation function is a mixture of exponentials and dampened sine waves after (p-q) lags.
#
arma11 = sm.tsa.ARMA(arma_rvs, (1,1)).fit()
resid = arma11.resid
r,q,p = sm.tsa.acf(resid, qstat=True)
data = np.c_[range(1,41), r[1:], q, p]
table = pandas.DataFrame(data, columns=['lag', "AC", "Q", "Prob(>Q)"])
print table.set_index('lag')
#
arma41 = sm.tsa.ARMA(arma_rvs, (4,1)).fit()
resid = arma41.resid
r,q,p = sm.tsa.acf(resid, qstat=True)
data = np.c_[range(1,41), r[1:], q, p]
table = pandas.DataFrame(data, columns=['lag', "AC", "Q", "Prob(>Q)"])
print table.set_index('lag')
#
# Exercise: How good of in-sample prediction can you do for another series, say, CPI
#
macrodta = sm.datasets.macrodata.load_pandas().data
macrodta.index = pandas.Index(sm.tsa.datetools.dates_from_range('1959Q1', '2009Q3'))
cpi = macrodta["cpi"]
#
# Hint:
#
fig = plt.figure(figsize=(12,8))
ax = fig.add_subplot(111)
ax = cpi.plot(ax=ax);
ax.legend();
#
# P-value of the unit-root test, resoundly rejects the null of no unit-root.
#
print sm.tsa.adfuller(cpi)[1]
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# In[ ]:
#
# < [Projects](http://nbviewer.jupyter.org/github/jckantor/Controlling-Natural-Watersheds/blob/master/notebooks/B.00-Projects.ipynb) | [Contents](toc.ipynb) | [Dashboard](http://nbviewer.jupyter.org/github/jckantor/Controlling-Natural-Watersheds/blob/master/notebooks/B.02-Dashboard.ipynb) >