def get_arima_prediction(price_data: np.ndarray,
order=Tuple[int, int, int],
allow_short: bool = True) -> int:
volatility = pd.Series(price_data).pct_change().std(ddof=1)
model = ARIMA(price_data, order=order)
forecast = model.fit().forecast(steps=1)
forecasted_returns = ((forecast - price_data[-1]) / price_data[-1])[0]
if forecasted_returns > 0.1 * volatility:
prediction = 1
elif forecasted_returns < -0.1 * volatility:
prediction = -1
else:
prediction = 0
if not allow_short:
prediction = max(prediction, 0)
assert prediction in [-1, 0, 1]
return prediction
def test_low_memory():
# Basic test that the low_memory option is working
endog = dta['infl'].iloc[:50]
mod = ARIMA(endog, order=(1, 0, 0), concentrate_scale=True)
res1 = mod.fit()
res2 = mod.fit(low_memory=True)
# Check that the models produce the same results
assert_allclose(res2.params, res1.params)
assert_allclose(res2.llf, res1.llf)
# Check that the model's basic memory conservation option wasn't changed
assert_equal(mod.ssm.memory_conserve, 0)
# Check that low memory was actually used (just check a couple)
assert_(res2.llf_obs is None)
assert_(res2.predicted_state is None)
assert_(res2.filtered_state is None)
assert_(res2.smoothed_state is None)
def in_sample_forecast(ts_data):
# training (70 % ) and test (30%)
training_data, test_data = ts_data[
0:int(len(ts_data) * TRAINING_DATA_SPLIT_RATIO
)], ts_data[int(len(ts_data) * TRAINING_DATA_SPLIT_RATIO):]
history = [x for x in training_data]
model_predictions = []
test_observations_num = len(test_data)
for time_point in range(test_observations_num):
model = ARIMA(history, order=ARIMA_ORDER)
model_fit = model.fit()
output = model_fit.forecast()
yhat = output[0]
model_predictions.append(yhat)
actual_value = test_data[time_point]
print(actual_value, yhat)
history.append(actual_value)
error = mean_squared_error(test_data, model_predictions)
return model_predictions, error
def predict_next(df, steps, time_range):
df = df.reset_index()
df_train, df_test = train_test_split(df, test_size=0.1, shuffle=False)
return_df = df_train
if time_range != 'Y':
history = [x for x in df_train.iloc[:, -1]]
model_predictions = []
model = ARIMA(history, order=(1, 1, 0), seasonal_order=(0, 1, 0, 24))
model_fit = model.fit()
steps = df_test.index.size + steps
predict_values = model_fit.get_forecast(steps=steps)
# print(predict_values.summary_frame())
[
model_predictions.append(x)
for x in predict_values.summary_frame()['mean']
]
date = df_train['Date'].iloc[-1]
for predict in model_predictions:
if time_range == 'W':
date += relativedelta(days=+7)
elif time_range == 'M':
date += relativedelta(months=+1)
# elif time_range == 'Y':
# date += relativedelta(years=+1)
if predict < 0 or math.isnan(predict):
predict = 0
return_df = return_df.append({
'Date': date,
'count': int(predict)
},
ignore_index=True)
model_error = metrics.mean_squared_error(
df_test['count'].tolist(),
return_df.loc[df_train.index.size + 1:df.index.size, 'count'].tolist())
return_df.set_index('Date', inplace=True)
return return_df, df_train.index.size, model_error
def get(self):
# Récupération du Dataset pour l'évaluation
df = get_data_cassandra()
print(df.head())
X = df['total_estimated_load'].values
# evaluate parameters (p,d,q) <=> (AR, I, MA)
p_values = 7
d_values = 0
q_values = 5
#best_cfg, best_score = evaluate_models(X, p_values, d_values, q_values)
best_cfg = (p_values,d_values,q_values)
# Entrainement du meilleur modèle
model = ARIMA(X, order=best_cfg)
model_fit = model.fit()
# save model
if not os.path.exists(model_local_path):
# Création du dossier d'export local qui n'existe pas
os.makedirs(model_local_path,exist_ok=False)
model_fit.save(model_local_path + model_name)
# Connexion au client HDFS
client = InsecureClient(url='http://namenode:9870', user='******')
# Création du dossier de stockage des fichiers traités
if client.status(model_hdfs_remote_path,strict=False) == None:
client.makedirs(model_hdfs_remote_path)
# Copie du modèle sur HDFS
remote_load_path = client.upload(model_hdfs_remote_path, model_local_path + model_name,overwrite=True)
#print(remote_load_path)
print(client.list(model_hdfs_remote_path))
return { 'best_cfg': best_cfg , 'status': 'Terminated'}
def fitted_vals_arima(self):
# ARIMA model
model_ARIMA = ARIMA(self.cases.astype(float), order=self.order)
ARIMA_fit = model_ARIMA.fit()
fitted_cases = ARIMA_fit.fittedvalues
latest_date = self.cases.last_valid_index() + datetime.timedelta(
days=1)
actual_cases = self.arima_model(self.cases, step=6)
fitted_values_cases = self.arima_model(fitted_cases, step=6)
actual_cases_float = float(actual_cases.loc[latest_date])
pred_cases_float = float(fitted_values_cases.loc[latest_date])
self.today_val = float(self.today_val)
print("today_val : " + str(self.today_val) + "prediction :" +
str(pred_cases_float))
try:
error = float(
abs((self.today_val - pred_cases_float) * 100) /
self.today_val)
error_pred = abs(error - 100)
except ZeroDivisionError:
error_pred = 0
print("today_val : " + str(self.today_val) + "actual :" +
str(actual_cases_float))
try:
error = float(
abs((self.today_val - actual_cases_float) * 100) /
self.today_val)
error_actual = abs(error - 100)
except ZeroDivisionError:
error_actual = 0
if error_actual > error_pred:
return error_actual, actual_cases.tail(6)
else:
return error_pred, fitted_values_cases.tail(6)
def ARIMA_predict(self, df):
'''
==Function==
Attain user inputs to decide ARIMA order
==Returns==
res = .fit()
atrain, atest = train and test set used for ARIMA
arima_title = title to be used in a plot
a_pred = predictions from ARIMA model
order = order used in ARIMA
'''
if self.order_method.lower() == 'predetermined':
order = (2, 0, 0)
elif self.order_method.lower() == 'auto':
order = self.auto_pdq(df)
elif self.order_method.lower() == 'manual':
print(
'CAUTION: MANUAL IS VERY COMPUTATIONALLY EXPENSIVE (~20 minutes) \nPlease enter "ok" to proceed'
)
confirmation = input()
if confirmation.lower() == 'ok':
print('Please hold')
order = self.best_order(df)
else:
print('Changing to Auto')
order = self.auto_pdq(df)
elif self.order_method.lower() == 'select':
print('Please input each parameter')
ord_p = int(input('p:'))
ord_d = int(input('d:'))
ord_q = int(input('q:'))
order = (ord_p, ord_d, ord_q)
atrain, atest = self.train_test(df)
atest_s, atest_e = atest.index.date[0], atest.index.date[-1]
atrain_s, atrain_e = atrain.index.date[0], atrain.index.date[-1]
res = ARIMA(df, order=order).fit()
a_pred = res.predict(atest_s, atest_e)
arima_title = f'ARIMA {order} MSE={round(mean_squared_error(atest,a_pred),5)}'
return res, atrain, atest, arima_title, a_pred, order
def evaluate_arima_model(X, arima_order):
# prepare training dataset
X = X.astype('float32')
train_size = int(len(X) * 0.50)
train, test = X[0:train_size], X[train_size:]
history = [x for x in train]
# make predictions
predictions = list()
for t in range(len(test)):
# difference data
months_in_year = 12
diff = difference(history, months_in_year)
model = ARIMA(diff, order=arima_order)
model_fit = model.fit()
yhat = model_fit.forecast()[0]
yhat = inverse_difference(history, yhat, months_in_year)
predictions.append(yhat)
history.append(test[t])
# calculate out of sample error
rmse = sqrt(mean_squared_error(test, predictions))
return rmse
def arima_man_forecast(ts, f, order, name):
plt.figure()
train = ts[:int(len(ts) * f)]
test = ts[int(len(ts) * f):]
model = ARIMA(train, order=order)
fit = model.fit()
plt.plot(train)
plt.plot(test)
forecast = fit.get_forecast(len(test))
plt.plot(forecast.predicted_mean)
ci = forecast.conf_int()
plt.fill_between(x=test.index,
y1=ci["lower Close Price"],
y2=ci["upper Close Price"],
color=(0.5, 0.5, 0.5, 0.2))
plt.title(name + " | [p,d,q] : " + str(order))
plt.legend(["train", "test", "forecast", "95% confidence"])
plt.show()
def evaluate_arima_model(X, arima_order):
# prepare training dataset
train_size = int(len(X.values) * 0.66)
train, test = X.values[0:train_size], X.values[train_size:]
history = [x for x in train]
predictions = list()
try:
for t in range(len(test)):
model = ARIMA(history, order=arima_order)
model_fit = model.fit()
yhat = model_fit.forecast()[0]
predictions.append(yhat)
history.append(test[t])
except:
pass
if len(test) > len(predictions):
error = mean_squared_error(test[:len(predictions)], predictions)
else:
error = mean_squared_error(test, predictions[:len(test)])
return error
def arima(df, *, ar, i, ma, fit=True, freq='B'):
"""
Create an ARIMA object for modeling time series.
Parameters:
- df: The dataframe containing the stock closing price as `close`
and with a time index.
- ar: The autoregressive order (p).
- i: The differenced order (q).
- ma: The moving average order (d).
- fit: Whether or not to return the fitted model,
defaults to `True`.
- freq: The frequency of the data. Default is 1 business day ('B').
Returns:
A `statsmodels` ARIMA object which you can use to fit and predict.
"""
arima_model = ARIMA(
df.close.asfreq(freq).fillna(method='ffill'), order=(ar, i, ma)
)
return arima_model.fit() if fit else arima_model
def perform_arima(train_X, train_y, model_name, order):
if model_name == 'arima':
model = ARIMA(train_y,
exog=train_X,
order=order,
enforce_invertibility=False,
enforce_stationarity=False)
elif model_name == 'sarimax':
model = SARIMAX(train_y,
exog=train_X,
order=order,
enforce_invertibility=False,
enforce_stationarity=False)
else:
raise KeyError(
'Invalid model selection, choose either "arima" or "sarimax"!')
results = model.fit()
return model, results
def __parameters_selection_arima(self, candles):
if len(candles) > 500:
return (1, 1, 1)
d = range(1, 3)
q = range(1, 3)
best_aic = float('inf')
best_params = ()
for i in d:
for j in q:
try:
model = ARIMA(candles, order=(i, 1, j))
model_fit = model.fit()
except:
continue
aic = model_fit.aic
if aic < best_aic:
best_aic = aic
best_params = (i, 1, j)
return best_params
def arima_model(data):
import pmdarima as pm
# model = pm.auto_arima(data, d=1, D=1,
# m=12, trend='c', seasonal=True,
# start_p=0, start_q=0, max_order=10, test='adf',
# stepwise=False, trace=True)
from statsmodels.tsa.arima.model import ARIMA
mod = ARIMA(data, order=(1, 1, 2), seasonal_order=(1, 1, 2, 12)).fit()
return mod
def grid_search(self):
best = np.inf
for p in range(1, 7):
for q in range(1, 7):
for P in range(1, 2):
for Q in range(1, 2):
model = ARIMA(
self.x,
order=(p, 1, q),
# seasonal_order=(P, 1, Q, 144),
dates=self.df.index,
)
model_fit = model.fit()
print("({},1,{}), ({},1,{},144) - AIC = {}.".format(
p, q, P, Q, model_fit.aic))
aic = model_fit.aic
if aic < best:
best = aic
best_model = (p, q)
print(best, best_model)
def fit(self, train):
series = train['Close']
# Record stock price series
self.history = series
auto_model = pm.auto_arima(series,
start_p=1,
start_q=1,
max_p=3,
max_q=3,
m=12,
start_P=0,
seasonal=False,
d=self.d,
D=1,
trace=True,
error_action='ignore',
suppress_warnings=True,
stepwise=True)
self.model = ARIMA(series, order=auto_model.order)
self.results = self.model.fit()
def q3_d():
print("begin")
df = get_data("data/HW5_WMT.xlsx", "HW5_WMT")
df.index = pd.to_datetime(df.index, format='%Y%m%d')
df['first_difference'] = np.log(df['WMT']) - np.log(df['WMT']).shift(1)
df['season_difference'] = np.log(df['WMT']) - np.log(df['WMT']).shift(4)
df_test = df.tail(len(df.index) - df.index.get_loc('2016-03-31'))
df_test = df_test.head(df_test.index.get_loc('2020-03-31'))
df_p = df.head(df.index.get_loc('2016-03-31'))
print(df_test)
rst_arima_list = []
rst_airline_list = []
i = 1
for index in df_test.index:
ARIMA_model = ARIMA(np.log(df_p['WMT']),
order=(0, 1, 1)).fit() # p=0, d=1, q=1
airline_model = ARIMA(np.log(df_p['WMT']),
order=(0, 1, 1),
seasonal_order=(0, 1, 1, 4)).fit()
rst_arima_list.append(ARIMA_model.forecast()[0])
rst_airline_list.append(airline_model.forecast()[0])
df_p = df.head(df.index.get_loc('2016-03-31') + i)
i += 1
plt.plot(df_test.index, rst_arima_list, label='ARIMA Model')
plt.plot(df_test.index, rst_airline_list, label='AIRLINE Model')
np.log(df_test['WMT']).plot(label='Reality')
plt.legend()
plt.show()
def AutoARIMA(stock) :
# Seasonality check
decomposed_df = decompose(stock) # TODO create new tab to plot the backend analysis
# The training will be carried out in logarithmic domain, to reduce data fluctuations and retrieve the best pqd triplet
dfClean = np.log(stock.stockValue['Close'])
# Split in train data and test data
train_data, test_data = dfClean[10:int(len(dfClean)*0.98)], dfClean[int(len(dfClean)*0.98):]
# AutoARIMA pdq identification
model_autoARIMA = auto_arima(train_data, start_p=5, start_q=5,
test='adf', # use adftest to find optimal 'd'
max_p=7, max_q=7, # maximum p and q
m=1, # frequency of series
d=None, # let model determine 'd'
seasonal=False, # No Seasonality
start_P=0,
D=0,
trace=True,
error_action='ignore',
suppress_warnings=True,
stepwise=True)
print(model_autoARIMA.summary())
# Train ARIMA Model
model = ARIMA(train_data, order=model_autoARIMA.order)
fitted = model.fit()
# Forecast
forecastedSteps = 15
model_predictions = fitted.get_forecast(steps=forecastedSteps)
forecasted_value = model_predictions.predicted_mean
forecasted_series = pd.Series(forecasted_value.values, index=test_data.index[:forecastedSteps])
confidence = model_predictions.conf_int(alpha=0.25) # 75% confidence
lower_series = pd.Series(confidence['lower Close'].values, index=test_data.index[:forecastedSteps])
upper_series = pd.Series(confidence['upper Close'].values, index=test_data.index[:forecastedSteps])
print('MAPE: {:.2%}'.format(np.mean(np.abs(forecasted_value.values - test_data[:forecastedSteps].values)/np.abs(test_data[:forecastedSteps].values))))
return forecasted_series, lower_series, upper_series
def arima(filename):
ts = open(filename)
tsA = ts.read().split('\n')
tsA = list(map(int, tsA))
#oepn the exel file
book = Workbook()
sheet = book.active
sheet['A1'] = "Predicted"
sheet['B1'] = "Expected"
sheet['C1'] = "Error"
now = time.strftime("%x")
sheet['A3'] = now
# split into train and test sets
size = int(len(tsA) * 0.66)
train, test = tsA[0:size], tsA[size:len(tsA)]
history = [x for x in train]
predictions = list()
# walk-forward validation
j = 2
for t in range(len(test)):
model = ARIMA(history, order=(5, 1, 0))
model_fit = model.fit()
output = model_fit.forecast()
yhat = output[0]
predictions.append(yhat)
obs = test[t]
history.append(obs)
sheet['A%d' % j] = yhat
sheet['B%d' % j] = obs
j += 1
# evaluate forecasts
rmse = sqrt(mean_squared_error(test, predictions))
sheet['C2'] = '%.3f' % rmse
book.save("sent.xlsx")
def test_invalid():
# Tests that invalid options raise errors
# (note that this is only invalid options specific to `ARIMA`, and not
# invalid options that would raise errors in SARIMAXSpecification).
endog = dta['infl'].iloc[:50]
mod = ARIMA(endog, order=(1, 0, 0))
# Need valid method
assert_raises(ValueError, mod.fit, method='not_a_method')
# Can only use 'statespace' with fixed parameters
with mod.fix_params({'ar.L1': 0.5}):
assert_raises(ValueError, mod.fit, method='yule_walker')
# Cannot override model-level values in fit
assert_raises(ValueError, mod.fit, method='statespace', method_kwargs={
'enforce_stationarity': False})
# start_params only valid for MLE methods
assert_raises(ValueError, mod.fit, method='yule_walker',
start_params=[0.5, 1.])
# has_exog and gls=False with non-statespace method
mod2 = ARIMA(endog, order=(1, 0, 0), trend='c')
assert_raises(ValueError, mod2.fit, method='yule_walker', gls=False)
# non-stationary parameters
mod3 = ARIMA(np.arange(100) * 1.0, order=(1, 0, 0), trend='n')
assert_raises(ValueError, mod3.fit, method='hannan_rissanen')
# non-invertible parameters
mod3 = ARIMA(np.arange(20) * 1.0, order=(0, 0, 1), trend='n')
assert_raises(ValueError, mod3.fit, method='hannan_rissanen')
def forecast_arima(df: pd.DataFrame, cols: list, with_graph: bool = True):
lag = 0
order = 1
moving_avg_model = 0
steps = 50
for col in cols:
model = ARIMA(df[col].iloc[:-steps],
order=(lag, order, moving_avg_model))
model_fit = model.fit()
model_for = model_fit.get_forecast(steps=steps, alpha=0.05)
print('\t==== Summary of forecast ARIMA(%d, %d, %d) ====\n' %
(lag, order, moving_avg_model))
print(model_for.summary_frame(), model_for.conf_int(), sep='\n')
print('RMSE: %f\nMAE: %f' %
(rmse(df[col][-50:], model_for.predicted_mean),
meanabs(df[col][-50:], model_for.predicted_mean)))
print()
if with_graph is True:
plt.figure(figsize=(12, 5))
plt.xlabel(col)
plt.title('Forecast for %s using ARIMA(%d, %d, %d)' %
(col, lag, order, moving_avg_model))
ax1 = model_for.predicted_mean.plot(color='blue',
grid=True,
label='Actual')
ax2 = df[col][-50:].plot(color='red',
grid=True,
secondary_y=True,
label='Estimated')
h1, l1 = ax1.get_legend_handles_labels()
h2, l2 = ax2.get_legend_handles_labels()
plt.legend(h1 + h2, l1 + l2, loc=2)
plt.show()
def test_mle(self):
# check predict with no constant, #3945
res1 = self.res1
endog = res1.model.endog
with pytest.warns(FutureWarning):
res0 = AR(endog).fit(maxlag=9, method='mle', trend='nc', disp=0)
assert_allclose(res0.fittedvalues[-10:], res0.fittedvalues[-10:],
rtol=0.015)
res_arma = ARIMA(endog, order=(9, 0, 0), trend="n").fit()
assert_allclose(res0.params, res_arma.params[:-1], rtol=1e-2)
assert_allclose(res0.fittedvalues[-10:], res_arma.fittedvalues[-10:],
rtol=1e-4)
def train():
# load data
series = read_csv('SmtExpMngr/models/train_data.csv',
header=None,
index_col=0,
parse_dates=True,
squeeze=True)
# prepare data
X = series.values
X = X.astype('float32')
# difference data
months_in_year = 12
diff = difference(X)
# fit model
model = ARIMA(diff, order=(0, 0, 1))
model_fit = model.fit()
# bias constant, could be calculated from in-sample mean residual
bias = 165.904728
# save model
model_fit.save('SmtExpMngr/models/model.pkl')
numpy.save('SmtExpMngr/models/model_bias.npy', [bias])
print("Tarined")
def arima_forecast(df):
"""Arima forecast."""
# Difference the data per week
days_in_week = 7
X = df.values
#differenced = difference(X, days_in_week)
differenced = X
model = ARIMA(differenced, order=(7, 1, 1)) # history
# fit the model
model_fit = model.fit()
# make forecast
now = arrow.utcnow()
today = now.format('YYYY-MM-DD')
future = now.shift(months=+3).format('YYYY-MM-DD')
start_index = today
end_index = future
forecast = model_fit.predict(
start=1, end=90)
list_results = []
[list_results.append(int(x)) for x in forecast]
return pd.DataFrame(list_results)
def arima(df, n_pred=5):
'''
Create forecasts by using ARIMA model
Input
------
df: Dataframe with stock data
Output
------
predicted_set: List of Predicted values
history_set : List of Historical values
'''
# number of values to be predicted
split = int(df.shape[0])
training_set = df.iloc[:split, 3:4].values
# Create and fit ARIMA model
hist_set = [x for x in training_set]
predicted_set = []
history_set = [item for sublist in training_set for item in sublist]
for time_point in range(n_pred):
model_init = ARIMA(hist_set, order=(4, 1, 0)) #(1,1,0)
#print(time_point)
model = model_init.fit()
forecast = model.forecast()
pred_value = forecast[0]
#print(pred_value)
predicted_set.append(pred_value)
hist_set.append([pred_value])
history_set.append(pred_value)
print('PREDICTED: ', predicted_set)
return predicted_set, history_set
def run_arima(chunked_data, price_col='y', n_prediction_units=1):
# supress trivial warnings from ARIMA
warnings.simplefilter('ignore', ConvergenceWarning)
# initialize a list to hold results (a list of dataframes)
results = []
# numerate through a list of chunked tuples, each having a pair of dataframes
for idx, (x_i, y_i) in enumerate(chunked_data):
# create ARIMA model based on x_i values
m = ARIMA(x_i[price_col].values, order=(0, 1, 0))
# fit the model
m_fit = m.fit()
# forecast for n_prediction_units
yhat = m_fit.forecast(steps=n_prediction_units)
# return a dataframe of targets and predictions of len targets
y_i['yhat'] = yhat[:len(y_i)]
# save results to a list and then return the list
results.append(y_i)
return results
def test_03(self):
ts_data = self.getData()
f_name='arima212_c_car_sold.pmml'
model = StateSpaceARIMA(ts_data,order=(2,1,2),trend = 'c')
result = model.fit()
StatsmodelsToPmml(result, f_name, conf_int=[95])
model_name = self.adapa_utility.upload_to_zserver(f_name)
z_pred = self.adapa_utility.score_in_zserver(model_name, {'h':5},'TS')
forecasts=result.get_forecast(5)
z_forecasts = list(z_pred['outputs'][0]['predicted_'+ts_data.squeeze().name].values())
model_forecasts = forecasts.predicted_mean.values.tolist()
z_conf_int_95_upper = list(z_pred['outputs'][0]['conf_int_95_upper_'+ts_data.squeeze().name].values())
model_conf_int_95_upper = forecasts.conf_int()['upper '+ts_data.squeeze().name].tolist()
z_conf_int_95_lower = list(z_pred['outputs'][0]['conf_int_95_lower_'+ts_data.squeeze().name].values())
model_conf_int_95_lower = forecasts.conf_int()['lower '+ts_data.squeeze().name].tolist()
self.assertEqual(np.allclose(z_forecasts,model_forecasts),True)
self.assertEqual(np.allclose(z_conf_int_95_upper, model_conf_int_95_upper),True)
self.assertEqual(np.allclose(z_conf_int_95_lower, model_conf_int_95_lower),True)
def arima_cross_validation(data,
order,
initial=12 * 15,
horizon=12,
period=6,
verbose=False):
k = (len(data) - initial - horizon) // period
if verbose: print('Cross validating over', str(k), 'folds.')
rmses = []
for i in range(1, k + 1):
n = len(data) - horizon - ((k - i) * period)
model = ARIMA(data[:n], order=order, freq='MS').fit()
y_hat = model.get_forecast(steps=horizon).predicted_mean.to_numpy()
y = data[n:n + horizon].to_numpy()
rmse = np.sqrt(mean_squared_error(y, y_hat))
if verbose:
print(
f'fold {i}: train[0:{n}], test[{n}:{n+horizon}] of {len(data)}, rmse={rmse}'
)
rmses.append(rmse)
return rmses
def get_sScore(pResiduals, kappa=252/30):
lCumulativeResiduals = pd.DataFrame(pResiduals.cumsum())
lCumulativeResiduals.index = lCumulativeResiduals.index.to_period('D')
m = pd.Series(index = lCumulativeResiduals.index)
sigma_eq = pd.Series(index = lCumulativeResiduals.columns)
for i in lCumulativeResiduals.columns:
lAR1Model = ARIMA(lCumulativeResiduals[i], order=(1,0,0))
lAR1 = lAR1Model.fit()
a = lAR1.params['const']
b = lAR1.params['ar.L1']
if -np.log(b) * 60 > kappa:
tmp = (lCumulativeResiduals[i]-lCumulativeResiduals[i].shift(1)* b)[1:]
a = tmp.mean()
central_a =tmp - a
m[i] = a/(1-b)
sigma_eq[i]=math.sqrt(central_a.var()/(1-b*b))
m = m.dropna()
m = m - m.mean()
Xt= lCumulativeResiduals.iloc[-1,:]
sigma_eq = sigma_eq.dropna()
s_score = (Xt-m)/sigma_eq
return s_score
def test_get_model_results():
from statsmodels.tsa.arima.model import ARIMA
import nb_credit_spread as cslibrary
cslib = cslibrary.creditspread()
start_date = '2009-01-31' #'1990-01-31' # '2009-01-31'
ytw_delta = cslib.get_ytw_from_date_delta(
start=start_date, srcfile='src/YTW-All-Values.xlsx')
endog_col = 'CS-Aaa-3MO-DCF'
order = (1, 1, 0)
endog, exog = ytw_delta[endog_col], None
model = ARIMA(endog=endog, exog=exog, order=order, trend='ct')
model_fit = model.fit()
import logging
# log = setlogging('test_get_model_results', logging.INFO)
log = old_setlogger('test_get_model_results', logging.INFO)
# logging.getLogger('test_get_model_results').info('test')
log.info(model_fit.summary())
if (log.hasHandlers()):
log.handlers.clear()