def predict_ARMA(df_lrt, time_index, days_to_forecast):
import pandas as pd
import statsmodels.tsa.api as smt
import numpy as np
import sys, os
# block printing did not work.
# sys.stdout = open(os.devnull, 'w')
df = df_lrt.iloc[(time_index - 30):time_index, :]
prediction = np.empty(len(df.columns))
i = 0
ind = time_index
for asset in df.columns:
tmp = df[asset]
try:
model = smt.ARMA(df[asset], (1,1))
model_fit = model.fit()
except:
model = smt.ARMA(df[asset], (0,0))
model_fit = model.fit()
prediction[i] = model_fit.forecast(steps = days_to_forecast)[0][-1]
i+=1
## could be improved by implementing auto arma to find ar and ma values.
res = prediction
res = (prediction - df.iloc[-1, :].values) / df.std(axis = 0)
#reenable printing
# sys.stdout = sys.__stdout__
return (res)
def _gdp_vol_estimate(self, variables):
"""
This function estimates conditional volatilities of GDP
growth
"""
raw_vol = tsa.ARMA(variables['gdp'], order=(1, 0)).fit(disp=False).resid
abs_vol = np.abs(raw_vol)
filtered_vol = tsa.ARMA(abs_vol, order=(1, 0)).fit(disp=False)
return filtered_vol.fittedvalues
def analyse_ts_arma(self, data):
ts = self.__logged_data(data).Close
best_ic = np.inf
best_order = None
best_mdl = None
rng = range(5) # orders greater than 5 are not practically useful
for i in rng:
for j in rng:
try:
tmp_mdl = smt.ARMA(ts, order=(i, j)).fit(
method='mle', trend='nc'
)
tmp_ic = tmp_mdl.bic # using bic here
logn('ic={}, order=({}, {})'.format(tmp_ic, i, j))
if tmp_ic < best_ic:
best_ic = tmp_ic
best_order = (i, j)
best_mdl = tmp_mdl
except: continue
logn(best_mdl.summary())
logn('using BIC', '='*20, sep='\n')
logn('ic: {:6.5f} | estimated order: {}'.format(best_ic, best_order))
logn('estimated alphas = {}'.format(best_mdl.arparams))
logn('estimated betas = {}'.format(best_mdl.maparams))
self.g.tsplot(best_mdl.resid,
lags=self.__n_lags,
saveas='ts_arma{}{}_residuals.png'.format(
best_order[0], best_order[1]
)
)
def learn_model_params(self, train_values):
self.__model = smt.ARMA(train_values, order=(0, self.__window_size))
self.__model_fit = self.__model.fit(maxlag=self.__window_size,
method='mle',
trend='c',
disp=-1)
self.__params = self.__model_fit.params
def create_arma(_df, store_items, filepath, exceptional):
extra_arr = []
for sno, ino in store_items:
df = _df[(_df.store_nbr == sno) & (_df.item_nbr == ino)].copy()
df = df.set_index('date2', drop=False)
df = df.sort_index()
start = datetime.datetime.strptime("2012-01-01", "%Y-%m-%d")
date_list = [
start + relativedelta(days=x) for x in range(0, len(df.index))
]
df['index'] = date_list
df.set_index(['index'], inplace=True)
df.index.name = None
try:
arma = tsa.ARMA(df.log1p, order=(1, 1))
extra_arr.append(len(date_list))
results = arma.fit()
print(sno, ino, len(date_list))
except:
exceptional.append([sno, ino])
else:
out = open(filepath + str(sno) + '_' + str(ino) + '.pkl', 'wb')
pickle.dump(results, out)
out.close()
# returns an array that contains number of dates it is trained on for each stationary item,store_no combo
return extra_arr
def best_arma(df) :
'''Find the Best ARMA model parameter for the returns'''
columns = [column for column in df]
a = [] # collect the arma order for the data
for i in columns :
best_aic = np.inf # Start point for AIC check, smallest aic wins
best_order = None
best_mdl = None
rng = range(1, 5) # set the biggest number for ARMA(p,q)
for j in rng :
for k in rng :
try :
tmp_mdl = smt.ARMA(df[i], order = (j, k)).fit(method = 'mle', trend = 'c')
tmp_aic = tmp_mdl.aic
print(tmp_aic)
if tmp_aic < best_aic :
best_aic = tmp_aic
best_order = (i, j, k)
best_mdl = tmp_mdl
except: continue
a += [best_order, best_mdl]
return np.array(a)
def delay_ARMA_model(filename, lag, delay):
value = get_input_output(filename)
output_data = value[0]
input_data = value[1]
best_aic = np.inf
best_order = None
best_delay = None
rng = range(lag)
dl = range(delay)
for d in dl:
output_tmp = output_data[d:]
input_tmp = input_data[:-(d)]
for i in rng:
for j in rng:
try:
tmp_mdl = smt.ARMA(np.array(output_tmp),
order=(i, j),
exog=np.array(input_tmp)).fit(
method='mle', trend='nc')
tmp_aic = tmp_mdl.aic
if tmp_aic < best_aic:
best_aic = tmp_aic
best_order = (i, j)
best_delay = d
except:
continue
print('aic: {:6.5f} | order: {}'.format(best_aic, best_order))
print('delay', best_delay)
def fit_ARMA(self, y, order_ar, order_ma, maxLag=30):
'''fit autoregression moving average (ARMA) model.
this function does not estimate the best coefficients.
Arguments:
----------
y : numpy array with signal
order_ar : order of autoregression (AR) linear model
order_ma : order of moving average (MA) linear model
maxlag : max lag
Return:
----------
mdl : model object '''
# if the mean of y is != 0, demean signal
if int(np.mean(y) != 0):
for t in range(len(y)):
y[t] = y[t] - np.mean(y)
u = np.random.randn(len(y), 2)
mdl = smt.ARMA(y, order=(order_ar, order_ma)).fit(maxlag=maxLag,
method='mle',
trend='nc',
exog=u)
print(mdl.summary())
return mdl
def bestfit_ARMA(self, y):
'''find order for AR and MA models: < Akaike Information Criterion (AIC)
the signal must be casual, stationary and invertible'''
best_aic = np.inf
best_order = None
best_mdl = None
u = np.random.randn(len(y), 2)
rng = range(5)
for i in rng:
for j in rng:
try:
tmp_mdl = smt.ARMA(y, order=(i, j)).fit(method='mle',
trend='nc',
exog=u)
tmp_aic = tmp_mdl.aic
if tmp_aic < best_aic:
best_aic = tmp_aic
best_order = (i, j)
best_mdl = tmp_mdl
except:
continue
print('aic: {:6.5f} | order: {}'.format(best_aic, best_order))
print best_mdl.summary()
return best_mdl
def fit_ARMAX(self, y, order_ar, order_ma, maxLag=30):
'''fit autoregression moving average (ARMA) model
NB: NEEDS FIXING..
Arguments:
---------
order_ar : order of autoregression (AR) linear model
order_ma : order of moving average (MA) linear model
maxlag : maximim lag
Return:
---------
mdl : model object '''
if int(np.mean(y) != 0):
for t in range(len(y)):
y[t] = y[t] - np.mean(y)
u = np.random.randn(len(y), 2)
mdl = smt.ARMA(y, order=(order_ar, order_ma)).fit(maxlag=maxLag,
method='mle',
trend='nc',
exog=u)
print(mdl.summary())
return mdl
def ARMA_Predict(df, order, symbols, t = 30) :
predict = list(0 for i in range(len(symbols)))
for i in range(len(symbols)) :
model = smt.ARMA(df[symbols[i]], order = (order[i][0], order[i][1])).fit(method = 'mle', trend = 'c')
predict[i] = model.forecast(steps = t)[0].reshape((t, 1))
predict[i] = np.mean(predict[i])
return predict
def ARMA_fit(endogenous, order, exog=None, trend='c'):
'''
例如：ARMA_fit(y,[3,0,1])
使用results.summary()打印结果，或者使用results.params或者 .resid/.pvalues
:param endogenous:
:param order:
:param trend:
:return:
'''
return tsa.ARMA(endogenous, order, exog=exog).fit(trend=trend)
def ma_model(code='399001'):
Y = get_stock_data(code)
max_lag = 30
result = smt.ARMA(Y.values, order=(0, 3)).fit(maxlag=max_lag,
method='mle',
trend='nc')
print(result.summary())
resid = pd.Series(result.resid, index=Y.index)
ts_plot(resid, lags=max_lag, title=code + 'MA拟合残差')
def _getStartingVals(self):
if self._data is not None:
if self._include_constant:
c = 'c'
else:
c = 'nc'
try:
model = sm.ARMA(
self._data.values,
(self._order['AR'], self._order['MA'])).fit(trend=c)
self._startingValues = model.params
except ValueError:
self._startingValues = None
else:
self._startingValues = np.zeros((self._pnum, )) + 0.5
def fit_arma_model_and_estimate_order(
self, data, order=(0, 1), maxlag=None,
method='mle', trend='nc', burnin=0
):
if maxlag is None:
maxlag = self.__n_lags
log('Fitting & estimating the ARMA model to the given data...')
mdl = smt.ARMA(data, order=order).fit(
maxlag=maxlag,
method=method,
trend=trend,
burnin=burnin
)
logn('[Done]')
logn(mdl.summary())
return mdl.arparams, mdl.k_ar, mdl.maparams, mdl.k_ma
def _gdp_vol_estimate(self, variables):
"""
This function estimates long-run volatilities of GDP growht. To esimate
such long-run volatility, I use a 2-sided rolling window to filter raw
volatility.
Returns:
--------
rolling_vol: pd.Series(float)
The rolling estimate of volatility
"""
raw_vol = tsa.ARMA(variables['gdp'], order=(1, 0)).fit(disp=False).resid
abs_vol = np.abs(raw_vol)
cycle, trend = sm_filters.tsa.filters.hpfilter(abs_vol)
return trend
def analyse_arma_p_q_best_ic(self, p=1, q=1):
n = 5000
burns = 2000
a, b, rts = self.get_sample_data(
m=SerialCorrelation.ModelType.arma, p=p, q=q, n=n, b=burns
)
self.g.tsplot(rts,
lags=self.__n_lags,
saveas='arma{}{}.png'.format(p, q)
)
# pick best order by minimum ic - aic or bic
# smallest ic value wins
best_ic = np.inf
best_order = None
best_mdl = None
rng = range(5)
for i in rng:
for j in rng:
try:
tmp_mdl = smt.ARMA(rts, order=(i, j)).fit(
method='mle', trend='nc'
)
tmp_ic = tmp_mdl.bic # using bic here
if tmp_ic < best_ic:
best_ic = tmp_ic
best_order = (i, j)
best_mdl = tmp_mdl
except: continue
logn(best_mdl.summary())
logn('using BIC', '='*20, sep='\n')
logn('true order: ({}, {})'.format(p, q))
logn('true alphas = {}'.format(a))
logn('true betas = {}'.format(b))
logn('ic: {:6.5f} | estimated order: {}'.format(best_ic, best_order))
logn('estimated alphas = {}'.format(best_mdl.arparams))
logn('estimated betas = {}'.format(best_mdl.maparams))
# analysing the model residuals with the estimated information
# the residuals should be a white noise process with no serial
# correlation for any lag, if this is the case then we can say
# that the best model has been fit to explain the data
self.g.tsplot(best_mdl.resid,
lags=self.__n_lags,
saveas='arma{}{}_residuals.png'.format(
best_order[0], best_order[1]
)
)
def getbestar(self, data, symbol):
max_lag = 30
mdl = smt.AR(data[symbol]).fit(maxlag=max_lag, ic='aic', trend='nc')
best_order = smt.AR(data[symbol]).select_order(maxlag=max_lag,
ic='aic',
trend='nc')
self.label_dikiful_2.setText(
'best estimated lag order = {}'.format(best_order))
max_lag = best_order
Y = data[symbol]
best_mdl = smt.ARMA(Y, order=(0, 3)).fit(maxlag=max_lag,
method='mle',
trend='nc')
if self.checkBox_forecast.isChecked():
self.forecast(data, symbol, best_mdl,
int(self.sdiffspinBox_2.text()))
elif not self.checkBox_forecast.isChecked():
self.tsplot(best_mdl.resid, symbol)
def MA():
n = int(1000)
# set the AR(p) alphas equal to 0
alphas = np.array([0.])
betas = np.array([0.6])
# add zero-lag and negate alphas
ar = np.r_[1, -alphas]
ma = np.r_[1, betas]
ma1 = smt.arma_generate_sample(ar=ar, ma=ma, nsample=n)
_ = tsplot(ma1, lags=30)
max_lag = 30
mdl = smt.ARMA(ma1, order=(0, 1)).fit(maxlag=max_lag,
method='mle',
trend='nc')
print(mdl.summary())
def _transform_arma(self, variables, order):
"""
uses an arma(p, 0, q) model to compute vol
Paramters:
-----------
variables: pd.DataFrame(float)
The (log) realized volatility.
order: tuple(int, int)
The p and q terms for the ARMA(p,q) process
Returns:
--------
res.fittedvalues pd.series(float)
The fitted values of the arma model
"""
res = tsa.ARMA(variables['vol'], order=order).fit(disp=False)
return res.fittedvalues
def _get_best_arma(TS):
best_aic = np.inf
best_order = None
best_mdl = None
for i in range(5):
for j in range(5):
try:
tmp_mdl = smt.ARMA(TS, order=(i, j)).fit(method='mle',
trend='nc',
freq=freq)
tmp_aic = tmp_mdl.aic
if tmp_aic < best_aic:
best_aic = tmp_aic
best_order = (i, j)
best_mdl = tmp_mdl
except:
continue
p('aic: {:6.5f} | order: {}'.format(best_aic, best_order))
return best_aic, best_order, best_mdl
def arima_pred(df, arima_inds):
arima_preds = df.copy()
for col in df.columns:
arr = df[col].dropna().values
arma = tsa.ARMA(arr, order=(0, 1, 0))
results = arma.fit()
res = results.predict(0, len(arr) - 1)
for item, ind in zip(res, arima_preds.index):
if np.isnan(arima_preds[col].loc[ind]):
continue
arima_preds[col].loc[ind] = item
ts = arima_preds[col]
mu = ts.mean()
sigma = ts.std()
for idx in ts.index:
if ts.loc[idx] > 3 * sigma + mu: # could be changed to 2*sigma + mu
arima_inds[col].loc[idx] = 1
return arima_inds
def evaluate_ARMA_models(y, AR_lim, MA_lim):
orders, aics = list(), list()
for i in range(1, AR_lim + 1):
for j in range(0, MA_lim + 1):
try:
mdlt = smt.ARMA(y, order=(i, j)).fit(method='mle',
trend='nc',
disp=-1)
orders.append((i, j))
aics.append(mdlt.aic)
except ValueError:
print(i, j, ' not convergent.')
except np.linalg.LinAlgError as e:
if "SVD did not converge" in str(e):
print(i, j, ' not convergent.')
else:
raise
assert len(orders) == len(aics), 'Error: Different lengths.'
print('Number of convergent models = ', len(orders))
aics, orders = zip(*sorted(zip(aics, orders)))
for a, b in zip(aics[:5], orders[:5]):
print(a, b)
return orders
def MA3():
import os
import sys
import pandas as pd
import pandas_datareader.data as web
import numpy as np
import statsmodels.formula.api as smf
import statsmodels.tsa.api as smt
import statsmodels.api as sm
import scipy.stats as scs
from arch import arch_model
import matplotlib.pyplot as plt
import matplotlib as mpl
get_ipython().magic('matplotlib inline')
p = print
end = '2017-01-01'
start = '2008-01-01'
get_px = lambda x: web.DataReader(x, 'yahoo', start=start, end=end)[
'Adj Close']
symbols = ['SPY', 'TLT', 'MSFT']
# raw adjusted close prices
data = pd.DataFrame({sym: get_px(sym) for sym in symbols})
# log returns
lrets = np.log(data / data.shift(1)).dropna()
def tsplot(y, lags=None, figsize=(10, 8), style='bmh'):
if not isinstance(y, pd.Series):
y = pd.Series(y)
with plt.style.context(style):
fig = plt.figure(figsize=figsize)
#mpl.rcParams['font.family'] = 'Ubuntu Mono'
layout = (3, 2)
ts_ax = plt.subplot2grid(layout, (0, 0), colspan=2)
acf_ax = plt.subplot2grid(layout, (1, 0))
pacf_ax = plt.subplot2grid(layout, (1, 1))
qq_ax = plt.subplot2grid(layout, (2, 0))
pp_ax = plt.subplot2grid(layout, (2, 1))
y.plot(ax=ts_ax)
ts_ax.set_title('Time Series Analysis Plots')
smt.graphics.plot_acf(y, lags=lags, ax=acf_ax, alpha=0.5)
smt.graphics.plot_pacf(y, lags=lags, ax=pacf_ax, alpha=0.5)
sm.qqplot(y, line='s', ax=qq_ax)
qq_ax.set_title('QQ Plot')
scs.probplot(y, sparams=(y.mean(), y.std()), plot=pp_ax)
plt.tight_layout()
return
# Fit MA(3) to SPY returns
max_lag = 30
Y = lrets.SPY
mdl = smt.ARMA(Y, order=(0, 3)).fit(maxlag=max_lag,
method='mle',
trend='nc')
p(mdl.summary())
_ = tsplot(mdl.resid, lags=max_lag)
ibm_df.Close_Price.plot()
# Plot ACF and PACF
ibm_df = ibm_df.dropna()
plot_acf(ibm_df.Close_Price, lags=50)
plot_pacf(ibm_df.Close_Price, lags=50)
# QQ plot and probability plot
sm.qqplot(ibm_df['Close_Price'], line='s')
# Optimize ARMA parameters
aicVal = []
for ari in range(1, 3):
for maj in range(0, 3):
arma_obj = smtsa.ARMA(ibm_df.Close_Price.tolist(),
order=(ari, maj)).fit(maxlag=30,
method='mle',
trend='nc')
aicVal.append([ari, maj, arma_obj.aic])
arma_obj_fin = smtsa.ARMA(ibm_df.Close_Price.tolist(),
order=(1, 0)).fit(maxlag=30,
method='mle',
trend='nc')
ibm_df['ARMA'] = arma_obj_fin.predict()
arma_obj_fin.summary()
# Plot the curves
f, axarr = plt.subplots(1, sharex=True)
f.set_size_inches(5.5, 5.5)
ibm_df['Close_Price'].iloc[1:].plot(color='b', linestyle='-', ax=axarr)
ibm_df['ARMA'].iloc[1:].plot(color='r', linestyle='--', ax=axarr)
# Generate MA(1) dataset
ar = np.r_[1, -0]
ma = np.r_[1, 0.7]
ma1_data = smtsa.arma_generate_sample(ar=ar, ma=ma, nsample=n)
plotds(ma1_data)
# Generate MA(2) dataset
ar = np.r_[1, -0]
ma = np.r_[1, 0.6, 0.7]
ma2_data = smtsa.arma_generate_sample(ar=ar, ma=ma, nsample=n)
plotds(ma2_data)
# Generate MA(3) dataset
ar = np.r_[1, -0]
ma = np.r_[1, 0.6, 0.7, 0.5]
ma3_data = smtsa.arma_generate_sample(ar=ar, ma=ma, nsample=n)
plotds(ma3_data)
# Build MA(1) model
ma1 = smtsa.ARMA(ma1_data.tolist(), order=(0, 1)).fit(maxlag=30,
method="mle",
trend="nc")
ma1.summary()
# Build MA(3) model
ma3 = smtsa.ARMA(ma3_data.tolist(), order=(0, 3)).fit(maxlag=30,
method="mle",
trend="nc")
ma3.summary()
mean1, mean2 = djia_df.iloc[:125].Close.mean(), djia_df.iloc[125:].Close.mean()
var1, var2 = djia_df.iloc[:125].Close.var(), djia_df.iloc[125:].Close.var()
print('mean1=%f, mean2=%f' % (mean1, mean2))
print('variance1=%f, variance2=%f' % (var1, var2))
# ADF Test
from statsmodels.tsa.stattools import adfuller
adf_result = adfuller(djia_df.Close.tolist())
print('ADF Statistic: %f' % adf_result[0])
print('p-value: %f' % adf_result[1])
# QQ plot and probability plot
sm.qqplot(djia_df['Close'], line='s')
# Optimize ARMA parameters (Will return a non-stationary error)
arma_obj = smtsa.ARMA(djia_df['Close'].tolist(),
order=(1, 1)).fit(maxlag=30, method='mle', trend='nc')
#Let us plot the original time series and first-differences
first_order_diff = djia_df['Close'].diff(1).dropna()
fig, ax = plt.subplots(2, sharex=True)
fig.set_size_inches(5.5, 5.5)
djia_df['Close'].plot(ax=ax[0], color='b')
ax[0].set_title('Close values of DJIA during Jan 2016-Dec 2016')
first_order_diff.plot(ax=ax[1], color='r')
ax[1].set_title('First-order differences of DJIA during Jan 2016-Dec 2016')
# plot signal
plotds(first_order_diff, nlag=50)
adf_result = adfuller(first_order_diff)
print('ADF Statistic: %f' % adf_result[0])
print('p-value: %f' % adf_result[1])
plot_pacf(dado_ar3['Preco']); # %% [markdown] # ## Ajuste dos modelos AR # %% dado_ar1_p1 = dado_ar1[0:500] dado_ar1_p2 = dado_ar1[500:600] # %% import statsmodels.tsa.api as smtsa # %% modelo_ar1 = smtsa.ARMA(dado_ar1_p1['Preco'], order=(1, 0)).fit() # %% modelo_previsto1 = modelo_ar1.predict(start=500,end=599) # %% plt.figure(figsize=(12,8)) plt.plot(dado_ar1_p1['Data'],dado_ar1_p1['Preco']) plt.plot(dado_ar1_p2['Data'],dado_ar1_p2['Preco']) plt.plot(dado_ar1_p2['Data'], modelo_previsto1,'r.') # %% dado_ar3_p1 = dado_ar3[0:500]
def main(job_no, sex, chroms, rank, dir):
start_time = time()
if not os.path.exists(dir):
os.makedirs(dir)
LOGGER.log_file_path = dir + "/" + str(
os.path.basename(__file__)) + job_no + ".log" # change
LOGGER.log_args()
LOGGER.log_message(get_file_hexdigest(__file__),
label="Hex digest of script.".ljust(17))
try:
LOGGER.log_message(str(os.environ['CONDA_DEFAULT_ENV']),
label="Conda environment.".ljust(17))
except KeyError:
pass
LOGGER.log_message('Name = ' + np.__name__ + ', version = ' +
np.__version__,
label="Imported module ")
LOGGER.log_message('Name = ' + pd.__name__ + ', version = ' +
pd.__version__,
label="Imported module ")
LOGGER.log_message('Name = ' + scipy.__name__ + ', version = ' +
scipy.__version__,
label="Imported module ")
LOGGER.log_message('Name = ' + statsmodels.__name__ + ', version = ' +
statsmodels.__version__,
label="Imported module ")
LOGGER.log_message('Name = ' + sklearn.__name__ + ', version = ' +
sklearn.__version__,
label="Imported module ")
pd.set_option('display.max_columns', None)
if chroms is None:
chroms = np.arange(1, 23).astype(str).tolist()
else:
chroms = chroms.split(',')
if rank:
LOGGER.log_message("%1d" % rank,
label="Rank of model to select (best=0).".ljust(30))
for chrom in chroms:
csv_name = dir + '/recomb_table_all_sexes_ch' + chrom + '.csv'
infile = open(csv_name, 'r')
LOGGER.input_file(infile.name)
infile.close()
data_table = pd.read_csv(csv_name, sep=',', index_col=0)
data_table = recombination.correct_missing_data(
data_table, 'LOCF', sex)
std_col = 'stdrate_' + sex
std_rates = data_table[std_col].values
variants_profiled = data_table.iloc[:, np.arange(5, 17)]
variant_counts = variants_profiled.sum(axis=1)
var_rates = variant_counts / 10000
print('\n\nChromosome number = ' + chrom)
print('Avge. mutation rate = ', np.mean(var_rates))
xvals = std_rates.reshape(-1, 1)
lmodel = LinearRegression()
lmodel.fit(xvals, var_rates)
residuals = var_rates - lmodel.predict(xvals)
sys.stdout.flush()
print('Slope, intercept, mean of residuals = ',
'%.8f' % lmodel.coef_[0], '%.8f' % lmodel.intercept_,
'%.12f' % np.mean(residuals))
orders = recombination.evaluate_ARMA_models(residuals, 10, 4)
best_order = orders[rank]
best_mdl = smt.ARMA(residuals, order=best_order).fit(method='mle',
trend='nc',
disp=0)
print(best_mdl.summary())
outfile_name = dir + '/ARMA_model_ch' + chrom + '_' + job_no + '.pklz'
recombination.save_model_details(best_mdl, outfile_name)
outfile = open(outfile_name, 'r')
LOGGER.output_file(outfile.name)
outfile.close()
duration = time() - start_time
LOGGER.log_message("%.2f" % (duration / 60.),
label="run duration (minutes)".ljust(30))
#
# ## Let's use a systematic approach to finding the order of AR and MA processes.
# In[23]:
# pick best order by aic
# smallest aic value wins
best_aic = np.inf
best_order = None
best_mdl = None
rng = range(5)
for i in rng:
for j in rng:
try:
tmp_mdl = smt.ARMA(arma22, order=(i, j)).fit(method='mle',
trend='nc')
tmp_aic = tmp_mdl.aic
if tmp_aic < best_aic:
best_aic = tmp_aic
best_order = (i, j)
best_mdl = tmp_mdl
except:
continue
print('aic: {:6.5f} | order: {}'.format(best_aic, best_order))
# ## We've correctly identified the order of the simulated process as ARMA(2,2).
#
# ### Lets use it for the sales time-series.
#
