def auto_arima_pyflux(ts_df):
import pyflux as pf
min_aic = np.inf
min_aic_param = None
for p in range(3):
for d in range(3):
for q in range(3):
if (p, d, q) != (0, 0, 0):
model = pf.ARIMA(data=ts_df, ar=p, integ=d, ma=q)
model_fit = model.fit("MLE") # M-H
if model_fit.aic < min_aic:
min_aic = model_fit.aic
min_aic_param = (p, d, q)
if min_aic_param is None:
print("Not successful in fitting ARIMA")
return -1
else:
model = pf.ARIMA(data=ts_df,
ar=min_aic_param[0],
integ=min_aic_param[1],
ma=min_aic_param[2])
model_fit = model.fit("MLE") # M-H
return min_aic, min_aic_param, model, model.predict(h=1,
intervals=True)
def plot(self, series, latent_variables = None):
series = series[180:360]
series = pd.DataFrame(series)
if latent_variables is None:
model = pf.ARIMA(series, self.ar, self.ma, self.integ)
else:
model = pf.ARIMA(series, self.ar, self.ma, self.integ, latent_variables)
model_fit = model.fit()
model.plot_predict(30, past_values = 100)
def _latent_variable_distribution(self, set_a, set_b):
a = np.asarray(set_a)
b = np.asarray(set_b)
a_model = pf.ARIMA(a, self.ar, self.ma, self.integ)
b_model = pf.ARIMA(b, self.ar, self.ma, self.integ)
a_modelfit = a_model.fit()
b_modelfit = b_model.fit()
a_vars = a_model.latent_variables
a_latent_vars = a_vars.dahlia()
a_vals = a_latent_vars[0]
a_indicators = a_latent_vars[1]
a_factors = a_latent_vars[2]
a_means = np.empty(len(a_vals))
a_sdevs = np.empty(len(a_vals))
b_vars = b_model.latent_variables
b_latent_vars = b_vars.dahlia()
b_vals = b_latent_vars[0]
b_indicators = b_latent_vars[1]
b_factors = b_latent_vars[2]
b_means = np.empty(len(b_vals))
b_sdevs = np.empty(len(b_vals))
for i in range(len(a_means)):
a_means[i] = a_vals[i].mean()
a_sdevs[i] = a_vals[i].std()
b_means[i] = b_vals[i].mean()
b_sdevs[i] = b_vals[i].std()
values = np.empty((len(a_vals), len(a_vals[0])))
for y in range(0, len(a_vals)):
"""datasets with the 'b' indicator are scaled differently,
use z-scores to average equivalent values in a and b distribution"""
if (b_indicators == 'b' and a_indicators[y] != 'b') or (a_indicators[y] == b_indicators[y] != 'b'):
for z in range(0, len(a_vals[0])):
pA = stat.norm(a_means[y],a_sdevs[y]).cdf(a_vals[y][z])
bVal = stat.norm(b_means[y], b_sdevs[y]).ppf(pA)
avg = (a_vals[y][z] + bVal) / 2
values[y][z] = avg
elif a_indicators[y] != 'b' and b_indicators[y] != 'b':
for z in range(0, len(a_vals[0])):
pB = stat.norm(b_means[y],b_sdevs[y]).cdf(b_vals[y][z])
aVal = stat.norm(a_means[y], a_sdevs[y]).ppf(pB)
avg = (b_vals[y][z] + aVal) / 2
values[y][z] = avg
else:
for z in range(0, len(a_vals[0])):
pA = stat.norm(a_means[y], a_sdevs[y]).cdf(a_vals[y][z])
bVal = stat.norm(b_means[y], b_sdevs[y]).ppf(pA)
avg = (a_vals[y][z] + bVal) / 2
values[y][z] = avg
return values
def a_test_bbvi_elbo():
"""
Tests that the ELBO increases
"""
model = pf.ARIMA(data=data, ar=1, ma=0, family=pf.Exponential())
x = model.fit('BBVI', iterations=200, record_elbo=True, map_start=False)
assert (x.elbo_records[-1] > x.elbo_records[0])
def run_aram(df, maxar, maxma, test_size=14):
data = df.dropna()
data['log'] = np.log(data[data.columns[0]])
# test_size = int(len(data) * 0.33)
train_size = len(data) - int(test_size)
train, test = data[:train_size], data[train_size:]
if test_stationarity(train[train.columns[1]]) < 0.01:
print('平稳，不需要差分')
else:
diffn = best_diff(train, maxdiff=8)
train = produce_diffed_timeseries(train, diffn)
print('差分阶数为' + str(diffn) + '，已完成差分')
print('开始进行ARMA拟合')
order = choose_order(train[train.columns[2]], maxar, maxma)
print('模型的阶数为：' + str(order))
_ar = order[0]
_ma = order[1]
model = pf.ARIMA(data=train,
ar=_ar,
ma=_ma,
target='diff',
family=pf.Normal())
model.fit("MLE")
test = test['payment_times']
test_predict = model.predict(int(test_size))
test_predict = predict_recover(test_predict, train, diffn)
RMSE = np.sqrt(
((np.array(test_predict) - np.array(test))**2).sum() / test.size)
print("测试集的RMSE为：" + str(RMSE))
def test_bbvi_mini_batch_elbo():
"""
Tests that the ELBO increases
"""
model = pf.ARIMA(data=data, ar=1, ma=1, family=pf.Cauchy())
x = model.fit('BBVI', iterations=100, mini_batch=32, record_elbo=True)
assert (x.elbo_records[-1] > x.elbo_records[0])
def a_test_predict_length():
"""
Tests that the prediction dataframe length is equal to the number of steps h
"""
model = pf.ARIMA(data=data, ar=2, ma=2, family=pf.Exponential())
x = model.fit()
assert (model.predict(h=5).shape[0] == 5)
def test_bbvi_elbo():
"""
Tests that the ELBO increases
"""
model = pf.ARIMA(data=data, ar=1, ma=1, family=pf.Skewt())
x = model.fit('BBVI',iterations=400, record_elbo=True)
assert(x.elbo_records[-1]>x.elbo_records[0])
def test_predict_is_nans():
"""
Tests that the in-sample predictions are not nans
"""
model = pf.ARIMA(data=data, ar=2, ma=2, family=pf.Skewt())
x = model.fit()
assert(len(model.predict_is(h=5).values[np.isnan(model.predict_is(h=5).values)]) == 0)
def univariate_arima():
'''
Reads the data and fits the ARIMA model
Prints the Acccuracy Score
Inputs:
None
Outputs:
None
'''
data = preprocessing.main()
n_train_hours = 52 * 3
train = data.iloc[:n_train_hours, :]
test = data.iloc[n_train_hours:, :]
model = pf.ARIMA(data=train, ar=9, ma=0, integ=1, target='milk')
x = model.fit("MLE")
x.summary()
# model.plot_fit(figsize=(15,5))
model.plot_predict(h=38, past_values=20, figsize=(15, 5))
#import pdb; pdb.set_trace()
yhat = model.predict(h=38)
pred_chg = yhat > 0
actual_chg = test.iloc[:-1, 0].diff() > 0
print accuracy_score(actual_chg, pred_chg)
def flux_auto(y, s, k, a, t, e, r):
""" One way to use flux package
- Contemporaneous y[1:] variables are used as exogenous 'X' in pmdarima
- This only works for k=1
:returns: x, s', w
"""
if s is None:
s = dict()
s = flux_hyperparams(s=s,r=r)
s = initialize_buffers(s=s,y=y)
if y is not None:
# Process observation and return prediction
assert isinstance(y, float) or len(y) == s['dim'], ' Cannot change dimension of input in flight '
y0, exog = split_exogenous(y=y, dim=s['dim'])
s = update_buffers(s=s, a=a, exog=exog, y0=y0)
if True: # Always fit prior to prediction
none_, s, _ = flux_auto(y=None, s=s, k=k, a=a, t=t, e=e, r=r) # Fit the model
assert none_ is None
return flux_or_last_value(s=s,k=k,exog=exog,y0=y0)
if y is None:
if len(s.get('buffer'))<s['n_burn']:
s['model'] = None
else:
data = pd.DataFrame(columns=['y'], data=s.get('buffer'))
s['model'] = pf.ARIMA(data=data, ar=s['ar'], ma=s['ma'], target='y', family=s['family'])
_ = s['model'].fit("MLE")
return None, s, None # Acknowledge that a fit was requested by returning x=None, w=None
def test_predict_is_length():
"""
Tests that the prediction IS dataframe length is equal to the number of steps h
"""
model = pf.ARIMA(data=data, ar=2, ma=2, family=pf.Cauchy())
x = model.fit()
assert (model.predict_is(h=5).shape[0] == 5)
def sliding_prediction_fixed_arma(ts, winsize=28, show_convg_info=False):
import pyflux as pf
from statsmodels.tsa.arima_model import ARIMA
ts = ts.astype(float) # statsmodel
# ts = pd.DataFrame(ts) # pyflux
start_indx = 0
predictions = []
for start_indx in range(0, ts.size - winsize):
# for start_indx in range(0, 1):
end_indx = start_indx + winsize
ts_sliced = ts[start_indx:end_indx]
date = ts.index[end_indx].date()
# statsmodel
# model = ARIMA(ts_sliced, (7, 1, 0))
# model_fit = model.fit(disp=show_convg_info)
# nextday_pred = model_fit.forecast(steps=1)
# pyflux
ts_sliced = pd.DataFrame(ts_sliced)
model = pf.ARIMA(data=ts_sliced, ar=7, integ=0, ma=1)
model_fit = model.fit("MLE") # M-H
nextday_pred = model.predict(h=1, intervals=True)
pred_count = nextday_pred['count'][0]
# print(model_fit)
# print(nextday_pred['count'][0])
print(nextday_pred['count'], date)
predictions.append((date, pred_count))
return pd.DataFrame(predictions)
def a_test_ppc():
"""
Tests PPC value
"""
model = pf.ARIMA(data=data, ar=2, ma=2, family=pf.Exponential())
x = model.fit('BBVI', iterations=100)
p_value = model.ppc(nsims=100)
assert (0.0 <= p_value <= 1.0)
def test_predict_length():
"""
Tests that the prediction dataframe length is equal to the number of steps h
"""
model = pf.ARIMA(data=data, ar=2, ma=2)
x = model.fit()
x.summary()
assert (model.predict(h=5).shape[0] == 5)
def test_ppc():
"""
Tests PPC value
"""
model = pf.ARIMA(data=data, ar=2, ma=2, family=pf.Cauchy())
x = model.fit('BBVI', iterations=100)
p_value = model.ppc()
assert (0.0 <= p_value <= 1.0)
def a_test_predict_nans():
"""
Tests that the predictions are not nans
"""
model = pf.ARIMA(data=data, ar=2, ma=2, family=pf.Exponential())
x = model.fit()
assert (len(
model.predict(h=5).values[np.isnan(model.predict(h=5).values)]) == 0)
def predict_request_body_len(train_set, test_set, method):
arima_utils.adfuller_test(train_set['request_body_len'].dropna())
arima_utils.plot_series(train_set['request_body_len'], 'Original Series')
'''
train_set['Value First Difference'] = train_set['request_body_len'] - train_set['request_body_len'].shift(1)
#dropdna, borra todos los vacios
arima_utils.adfuller_test(train_set['Value First Difference'].dropna())
arima_utils.plot_series(train_set['Value First Difference'], 'Value First Difference')
'''
arima_utils.plot_pacf(train_set['request_body_len'])
arima_utils.plot_acf(train_set['request_body_len'])
# use request_body_len, response_body_len
# usar p = 12 (intento original)
# usar q = 34 (intento original)
p = 2
q = 1
start_time = time.time()
print("STARTING TIMER REQUEST ", method)
model = pf.ARIMA(data=train_set,
ar=p,
ma=q,
integ=0,
target='request_body_len')
x = model.fit(method=method)
end_time = time.time()
total_time = end_time - start_time
print("TIME: ", total_time)
# PRINT DATA
#print(x.summary())
print(x.scores)
model.plot_fit()
plt.show()
# model.plot_predict_is(h=30)
# firstRegister = conn.head(30)
#plt.plot(test_set['ts'], test_set['request_body_len'])
#model.plot_predict_is(h=100, past_values=40)
#print(model.predict(h=100))
start_time = time.time()
print("STARTING TIMER, PREDICT REQUEST ", method)
plt.plot(test_set.index,
test_set['request_body_len'],
label='REAL',
color='pink')
plt.plot(model.predict(h=100), label='PREDICTION', color='cyan')
plt.legend(['REAL', 'PREDICTION'])
# model.plot_predict(h=200, past_values=40)
# plt.plot(firstRegister['ts'], firstRegister['request_body_len'])
end_time = time.time()
total_time = end_time - start_time
print("TIME: ", total_time)
plt.show()
def test_sample_model():
"""
Tests sampling function
"""
model = pf.ARIMA(data=data, ar=2, ma=2, family=pf.Cauchy())
x = model.fit('BBVI', iterations=100)
sample = model.sample(nsims=100)
assert (sample.shape[0] == 100)
assert (sample.shape[1] == len(data) - 2)
def test_predict_is_nonconstant():
"""
We should not really have predictions that are constant (should be some difference)...
This captures bugs with the predict function not iterating forward
"""
model = pf.ARIMA(data=data, ar=2, ma=2, family=pf.Cauchy())
x = model.fit()
predictions = model.predict_is(h=10, intervals=False)
assert (not np.all(predictions.values == predictions.values[0]))
def ARIMAX_model(df, target, ar, integ, ma):
pfarima_model = pf.ARIMA(data=df,
ar=ar,
ma=ma,
integ=integ,
target=target,
family=pf.Normal())
arima_x_mh = pfarima_model.fit("M-H")
arima_x_mh.summary()
def test_predict_nans():
"""
Tests that the predictions are not nans
"""
model = pf.ARIMA(data=data, ar=2, ma=2)
x = model.fit()
x.summary()
assert (len(
model.predict(h=5).values[np.isnan(model.predict(h=5).values)]) == 0)
def predict_response_body_len(train_set, test_set):
arima_utils.adfuller_test(train_set['response_body_len'])
arima_utils.plot_series(train_set['response_body_len'], 'Original Series')
arima_utils.plot_pacf(train_set['response_body_len'])
arima_utils.plot_acf(train_set['response_body_len'])
# use request_body_len, response_body_len
# usar p = 8 (intento original)
# usar q = 7 (intento original)
p = 2
q = 8
start_time = time.time()
print("STARTING TIMER, RESPONSE")
model = pf.ARIMA(data=train_set,
ar=p,
ma=q,
integ=0,
target='response_body_len')
x = model.fit(method=method)
#model.fit(method='BBVI', iterations='10000', optimizer='ADAM') ###
#model.fit(method='Laplace')
#model.fit(method='M-H')
end_time = time.time()
total_time = end_time - start_time
print("TIME: ", total_time)
# PRINT DATA
print(x.summary())
print(x.scores)
model.plot_fit()
plt.show()
# model.plot_predict_is(h=30)
# firstRegister = conn.head(30)
start_time = time.time()
print("STARTING TIMER PREDICT RESPONSE")
plt.plot(test_set.index,
test_set['response_body_len'],
label='REAL',
color='pink')
plt.plot(model.predict(h=100), label='PREDICTION', color='cyan')
plt.legend(['REAL', 'PREDICTION'])
#end_time = time.time()
#total_time = end_time - start_time
#print("TIME: " , total_time)
end_time = time.time()
total_time = end_time - start_time
print("TIME: ", total_time)
# model.plot_predict(h=200, past_values=40)
# plt.plot(firstRegister['ts'], firstRegister['response_body_len'])
plt.show()
def doARMA(data, ar, ma):
family = pf.Normal()
model = pf.ARIMA(data=data, ar=ar, ma=ma, target='sunspot.year', family=family)
x = model.fit("MLE")
# x.summary()
# model.plot_fit(figsize=(15,10))
model.plot_predict_is(h=500, figsize=(15,5))
# model.plot_predict(h=20,past_values=20,figsize=(15,5))
res = model.predict_is(h=500)
return res
def predict_ARIMA(trainData, testX, lookAhead, p, q):
testX = np.array(testX).reshape(-1)
total_train = np.concatenate([trainData, testX], axis=0)
model = pf.ARIMA(data=total_train, ar=p, ma=q, family=pf.Normal())
model.fit(method="MLE")
pred = model.predict(lookAhead, intervals=False)
return pred
def a_test_bbvi():
"""
Tests an ARIMA model estimated with BBVI and that the length of the latent variable
list is correct, and that the estimated latent variables are not nan
"""
model = pf.ARIMA(data=data, ar=1, ma=0, family=pf.Exponential())
x = model.fit('BBVI', iterations=200)
assert (len(model.latent_variables.z_list) == 3)
lvs = np.array([i.value for i in model.latent_variables.z_list])
assert (len(lvs[np.isnan(lvs)]) == 0)
def a_test_laplace():
"""
Tests an ARIMA model estimated with Laplace approximation and that the length of the
latent variable list is correct, and that the estimated latent variables are not nan
"""
model = pf.ARIMA(data=data, ar=1, ma=1, family=pf.Exponential())
x = model.fit('Laplace')
assert (len(model.latent_variables.z_list) == 3)
lvs = np.array([i.value for i in model.latent_variables.z_list])
assert (len(lvs[np.isnan(lvs)]) == 0)
def test_pml():
"""
Tests a PML model estimated with Laplace approximation and that the length of the
latent variable list is correct, and that the estimated latent variables are not nan
"""
model = pf.ARIMA(data=data, ar=1, ma=1, family=pf.Cauchy())
x = model.fit('PML')
assert (len(model.latent_variables.z_list) == 4)
lvs = np.array([i.value for i in model.latent_variables.z_list])
assert (len(lvs[np.isnan(lvs)]) == 0)
def test_mh():
"""
Tests an ARIMA model estimated with Metropolis-Hastings and that the length of the
latent variable list is correct, and that the estimated latent variables are not nan
"""
model = pf.ARIMA(data=data, ar=1, ma=1, family=pf.Cauchy())
x = model.fit('M-H', nsims=300)
assert (len(model.latent_variables.z_list) == 4)
lvs = np.array([i.value for i in model.latent_variables.z_list])
assert (len(lvs[np.isnan(lvs)]) == 0)
def test_bbvi_mini_batch():
"""
Tests an ARIMA model estimated with BBVI and that the length of the latent variable
list is correct, and that the estimated latent variables are not nan
"""
model = pf.ARIMA(data=data, ar=1, ma=1, family=pf.Cauchy())
x = model.fit('BBVI', iterations=100, mini_batch=32)
assert (len(model.latent_variables.z_list) == 4)
lvs = np.array([i.value for i in model.latent_variables.z_list])
assert (len(lvs[np.isnan(lvs)]) == 0)
