def grid_search_best_model_timeseries_arma(df, grid, cv):
keys, values = zip(*grid.items())
params = []
for v in product(*values):
params.append(tuple(v))
print(params)
best_param = None
best_score = np.infty
tsp = TimeSeriesSplit(n_splits=cv)
for param in params:
scores = []
for train_ind, test_ind in tsp.split(df):
train_data = df.iloc[train_ind]
test_data = df.iloc[test_ind]
try:
#print(train_data, test_data)
estimator = arima_model.ARMA(train_data, order=param)
res = estimator.fit()
#print(res.params)
#get out of sample predictions with test data start and end
y_pred = estimator.predict(res.params, test_data.index[0],
test_data.index[-1])
#print(y_pred)
y_test = test_data.values.reshape(-1)
score = math.sqrt(metrics.mean_squared_error(y_test, y_pred))
scores.append(score)
except:
pass
#print(scores)
if len(scores) > 0 and np.mean(scores) < best_score:
best_score = np.mean(scores)
best_param = param
if best_param is not None:
estimator = arima_model.ARMA(df, order=best_param)
res = estimator.fit()
print("best parameters:" + str(best_param))
print("validation rmse:" + str(best_score))
#get insample predictions with start and end indices
y_pred = estimator.predict(res.params, start=0, end=df.shape[0] - 1)
y_train = df.values.reshape(-1)
train_rmse = math.sqrt(metrics.mean_squared_error(y_train, y_pred))
print("train rmse:" + str(train_rmse))
return estimator, res
else:
return None, None
def _fit(self, ord, ix, if_plot=False):
assert len(
self.y[ix]) > 2 * (ord[0] + ord[1]), 'not enough data to fit'
from statsmodels.tsa import arima_model
ar = arima_model.ARMA(self.y[ix], ord)
param = ar.fit(trend='nc')
if if_plot:
param.plot_predict()
return param.params
def fit_ARIMA(self, col, order=(1, 1)):
try:
model = ARIMA.ARMA(self.ss[col])
result = model.fit(order=order)
self.ss['%s_ARIMA_fitted' % col] = result.fittedvalues
self.ss['%s_ARIMA_resid' % col] = result.resid
except KeyError:
print "Warning: %s is a bad key, ignoring" % col
def garch_group(Y, q0=1, p=1, q=1, do_plots=False):
residuals = np.zeros(Y.shape[0] - q0)
for y in np.transpose(Y):
model = ar_model.AR(y)
results = model.fit(q0)
et = results.resid**2
residuals += (et - sum(et) / len(et)) / np.std(et)
residuals /= Y.shape[1]
model = arima_model.ARMA(residuals, (p, q))
r2 = model.fit()
if do_plots:
print r2.pvalues
pylab.plot(r2.fittedvalues)
pylab.show()
else:
return residuals, r2
def arma_construct(p, q, x_train, y_train, x_valid, y_valid, num_data):
# This is a generic ARMA model constructor, which is used to estimate certain ARMA models on a set of data.
# Inputs: p - The order of the AR model.
# q - The order of the MA model.
# x_train - Partition of noise set for training.
# y_train - Training data evaluated using original model.
# x_valid - Partition of noise set for validation.
# y_valid - Validation data evaluated using original model.
# num_data - Amount of data being generated.
# Outputs: No outputs, put does do a lot of print statements.
# First, we create the model.
arma = arima_model.ARMA(y_train, order=(p, q))
arma_fit = arma.fit(disp=0)
# Then, we convert the parameters to lists, so they match the format of the original construction.
ma_co = arma_fit.maparams.tolist()
ar_co = list(-arma_fit.arparams)
# We also need to add in a 1 at the start, since those are neglected when presenting the results from
# the ARMA function we use.
ma_co.insert(0, 1)
ar_co.insert(0, 1)
# Getting the coefficients of the AR and MA models, separately.
print('**** ARMA({0},{1}) ****'.format(p, q))
print('AR Coefficients: ', ["%.3f" % item for item in ar_co])
print('MA Coefficients: ', ["%.3f" % item for item in ma_co])
# Next, we calculate the error for the model.
# Starting with the training error.
arma_train = signal.lfilter(ma_co, ar_co, x_train)
ave_t_error = (1 / (num_data / 2)) * np.sum(np.abs((y_train - arma_train) / arma_train))
print('Training Error: %.3f' % ave_t_error)
# Then we calculate the validation error.
arma_valid = signal.lfilter(ma_co, ar_co, x_valid)
ave_v_error = (1 / (num_data / 2)) * np.sum(np.abs((y_valid - arma_valid) / arma_valid))
print('Validation Error: %.3f' % ave_v_error)
def fit_row(self, row):
model = arima_model.ARMA(row, (self.p, self.q)).fit()
pred = model.predict(start=0, end=len(row) - 1)
params = model.params
return (pred, params)
def garch(y, q0=1, p=1, q=1):
model = ar_model.AR(y)
results = model.fit(q0)
et = results.resid**2
model = arima_model.ARMA((et - sum(et) / len(et)) / np.std(et), (p, q))
return model.fit()
print(' Coefficents of MA: {0}\n'.format(formatIter(paraMA)))
print(' Sigma^2: %.4f\n' % paraSigma2)
print(' Method：Moment Estimation / Inverse Correlation Function Method')
'''
## (2)
use arima_model.ARMA.fit() to compute MLE
we may encounter warnings, i.e. non-positive definite Hessian
，but ignore it
we select start_param of optimization algorithm by arima_model.ARMA.fit()
instead of results of (1)
'''
filterwarnings('ignore') # ignore warnings
while True:
series = arima_process.arma_generate_sample(ar, ma, 100, sigma=2)
armaModel = arima_model.ARMA(series, (4, 2))
try:
armaResult = armaModel.fit(method='mle', trend='nc',
disp=0, maxiter=10000)
except ValueError as e:
continue
else:
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')
self.reactor_size = (24 * 1.0, 'in^3')
else:
self.reactor_size = (None, None)
def _load_timeseries_data(self):
self.gts = df.Dataframe()
self.gts.SQL_load_data(
self.interface_proc,
'gas_proc_data_tbl',
conditions=[
"timestamp >= '%s'" % self.run_info.info['ss_start'],
"timestamp < '%s'" % self.run_info.info['ss_stop']
]) #This line needs to automatically load the units
if __name__ == "__main__":
user = raw_input('User: '******'timestamp']
model = ARIMA.ARMA(test.gts['mass_flow_brush_feeder'], order=(1, 1))
result = model.fit()
print result.summary()
print test.gts['mass_flow_brush_feeder']
print result.fittedvalues
print result.resid
data = p.read_csv(src, sep="|")
#print(data) # MUST GIVE COLUMN HEADERS in CSV FILE BEFORE WE DO THIS
five_year_data = data[["5Y"]]
print(five_year_data) # how do we get this to work as a 1D array?
#five_year_data_1d = p.Series.ravel(five_year_data)
#print(five_year_data_1d)
acf = calc_acf(five_year_data)
print(acf[0])
print("ACF is", acf)
# plt.xlim(1, 10)
acf_plt = s.plot_acf(acf)
pacf = calc_pacf(five_year_data, 10)
print(pacf)
pacf_plt = s.plot_pacf(pacf)
plt.show()
# Param estimation
#model = a(five_year_data, order=(10,1,0))
# model_fit = model.fit(disp=0)
# print(model_fit.summary())
ar_model = ar.AR(five_year_data)
ar_model_fit = ar_model.fit(10)
print("Params")
print(ar_model_fit.params)
ma_model = ma.ARMA(five_year_data.values, (0, 10))
ma_model_fit = ma_model.fit()
print("MA Params")
print(ma_model_fit.params)
def fit_row(self, row: np.ndarray) -> base.ApproxAndParams:
model = arima_model.ARMA(row, (self._p, self._q)).fit()
predicted = model.predict(start=0, end=len(row) - 1)
params = model.params
return base.ApproxAndParams(predicted, params)
method='SLSQP',
bounds=bnds,
options={'maxiter': MAX_MLE_ITER})
time_mymle_end = time.time()
time_mymle = time_mymle_end - time_mymle_start
assert res.success
mu_hat = res.x[0]
phi_hat = res.x[1]
theta_hat = res.x[2]
sigma2_hat = res.x[3]
### Now, for comparison purposes, estimate using the StatsModels package ###
sm = arima_model.ARMA(y_vec, order=(1, 1))
time_sm_start = time.time()
fittedsm = sm.fit(disp=0)
time_sm_end = time.time()
time_sm = time_sm_end - time_sm_start
### Display results ###
print('(mu, phi, theta, sigma2) = (%f, %f, %f, %f)' % (mu, phi, theta, sigma2))
print('(mu, phi, theta, sigma2)_0 = (%f, %f, %f, %f)' %
(mu_guess, phi_guess, theta_guess, sigma2_guess))
print('(mu, phi, theta, sigma2)^hat = (%f, %f, %f, %f)' %
(mu_hat, phi_hat, theta_hat, sigma2_hat))
print('(mu, phi, theta, sigma2)^hatSM = (%f, %f, %f, %f)' %
plt.show()
autocorrelation_plot(dfLeadsTrain['Count_Tickets'])
plt.show()
# In[10]:
print dfLeadsTrain.columns
print dfLeadsTest.tail()
# In[11]:
exog_cols = [
u'Day_of_Week_1', u'Day_of_Week_2', u'Day_of_Week_3', u'Day_of_Week_4',
u'Day_of_Week_5', u'Day_of_Week_6', u'MONTH_END', u'HOLIDAY'
]
modelARMA = am.ARMA(dfLeadsTrain.Count_Tickets, (2, 1),
exog=dfLeadsTrain[exog_cols])
resultsARMA = modelARMA.fit()
print(resultsARMA.summary())
# Durbin-Watson on results shows no/low positive autocorrelation
# In[12]:
print resultsARMA.aic, resultsARMA.bic, resultsARMA.hqic
print sm.stats.durbin_watson(resultsARMA.resid.values)
# In[13]:
fig = plt.figure(figsize=(12, 8))
ax = fig.add_subplot(111)
ax = resultsARMA.resid.plot(ax=ax)
y = util.simulate(poly=100, sinusoids=(10, 100, -20)).values
hr = np.arange(365 * 96) * .25
t = hr * 3600
sinusoids = [
np.random.normal(0.0, 0.1, 365 * 96) + 10 +
3 * np.sin(hr * 2 * np.pi / 96 / .25),
np.random.normal(0.0, 0.1, 365 * 96) + 15 +
3 * np.sin(hr * 2 * np.pi / 96 / .25) +
3 * np.cos(t * 2 * np.pi / 96. / .25 / 365.),
np.random.normal(0.0, 1.0, 365 * 96) + 15 +
3 * np.sin(hr * 2 * np.pi / 96 / .25) +
3 * np.cos(t * 2 * np.pi / 96. / .25 / 365.) +
np.random.normal(0.0, 1e-5, 365 * 96).cumsum()
]
arma20 = arima_model.ARMA(y, (2, 0)).fit()
y2 = arma.predict(start=10 * 96, end=12 * 96)
y1 = y[10 * 96 - 1:12 * 96]
plt.plot(t[10 * 96 - 1:12 * 96], zip(*[y1, y2]))
plt.show()
y2 = arma30.predict(start=10 * 96, end=12 * 96)
plt.plot(t[10 * 96 - 1:12 * 96], zip(*[y1, y2]))
plt.show()
arma30.resid.plot()
plt.plot(arma30.resid)
plt.show()
plt.plot(arma30.resid / y2)
plt.plot(arma30.resid / y)
plt.show()
plt.plot(arma30.resid / y)
plt.show()
# stats: -0.582869706432, pvalue: 0.920441499758
# h0: rho == 1 is rejected. There is no unit root.
# for AAPL
# MA(15) fits model the best.
# stats: 3.04749309416, pvalue: 1.0
# stats: 5.14080783224, pvalue: 0.999999999468
# h0: rho == 1 is not rejected. There is unit root.
test_series = goog_df
from arch.unitroot import PhillipsPerron
# fit MA on resid.
import statsmodels.tsa.arima_model as arma
for ma_lag in [7, 8, 9, 10, 11, 12, 13, 15, 20]:
model = arma.ARMA(test_series, (0, ma_lag)).fit()
print 'lag: {}, aic: {}'.format(ma_lag, model.aic)
pp = PhillipsPerron(test_series, trend='c', lags=10, test_type='tau')
print 'stats: {}, pvalue: {}'.format(pp.stat, pp.pvalue)
pp = PhillipsPerron(test_series, trend='c', lags=10, test_type='rho')
print 'stats: {}, pvalue: {}'.format(pp.stat, pp.pvalue)
### using adf test
### goog and aapl t-value
# 0.876758903592
# 0.999070159449
test_series = goog_df
from arch.unitroot import ADF
adf = ADF(goog_df, lags=24)
print adf.pvalue
def get_arma_coefficients(series, order=(2, 3)):
"""
Returns the ARMA model coefficients for the given model
"""
model = arima_model.ARMA(series, order).fit(disp=False)
return model.params