def regression_for_states(X_s, y_s):
X_s = sm.add_constant(X_s)
from_ = 5
prd = [np.mean(y_s) for _ in range(from_)]
#tscv = TimeSeriesSplit(n_splits= len(y_s)-from_)
#for train_index, test_index in tscv.split(X_s):
# X_train, X_test = X_s[train_index], X_s[test_index]
# y_train, y_test = y_s[train_index], y_s[test_index]
mod = sm.RecursiveLS(y_s, X_s)
res = mod.fit()
#prd.append(res.predict(X_test))
prd = [res.predicted_state[i, i] for i in range(y_s.shape[0])]
resid = y_s.flatten() - prd
prd = np.array(prd)
resid = y_s.flatten() - prd
X = np.arange(len(y_s))[:, np.newaxis]
kernel = 1.0 * RBF(length_scale=1.0, length_scale_bounds=(1e-2, 1e3)) \
+ WhiteKernel(noise_level=1e-5, noise_level_bounds=(1e-10, 1e-1))
gp = GaussianProcessRegressor(kernel=kernel, alpha=0.0).fit(X, resid)
X_ = np.linspace(0, 12, 12)
y_mean, y_cov = gp.predict(X_[:, np.newaxis], return_cov=True)
return ({'pred': prd + y_mean, 'var': y_cov})
def recursivels_model():
# Recursive Least Squares
dta = sm.datasets.copper.load_pandas().data
dta.index = pd.date_range("1951-01-01", "1975-01-01", freq="AS")
endog = dta.WORLDCONSUMPTION
# To the regressors in the dataset, we add a column of ones for an intercept
exog = sm.add_constant(
dta[["COPPERPRICE", "INCOMEINDEX", "ALUMPRICE", "INVENTORYINDEX"]] # pylint: disable=E1136
)
rls = sm.RecursiveLS(endog, exog)
model = rls.fit()
inference_dataframe = pd.DataFrame([["1951-01-01", "1975-01-01"]], columns=["start", "end"])
return ModelWithResults(model=model, alg=rls, inference_dataframe=inference_dataframe)
dta.index = pd.date_range('1951-01-01', '1975-01-01', freq='AS')
endog = dta['WORLDCONSUMPTION']
# To the regressors in the dataset, we add a column of ones for an
# intercept
exog = sm.add_constant(
dta[['COPPERPRICE', 'INCOMEINDEX', 'ALUMPRICE', 'INVENTORYINDEX']])
# First, construct and fit the model, and print a summary. Although the
# `RLS` model computes the regression parameters recursively, so there are
# as many estimates as there are datapoints, the summary table only presents
# the regression parameters estimated on the entire sample; except for small
# effects from initialization of the recursions, these estimates are
# equivalent to OLS estimates.
mod = sm.RecursiveLS(endog, exog)
res = mod.fit()
print(res.summary())
# The recursive coefficients are available in the `recursive_coefficients`
# attribute. Alternatively, plots can generated using the
# `plot_recursive_coefficient` method.
print(res.recursive_coefficients.filtered[0])
res.plot_recursive_coefficient(range(mod.k_exog), alpha=None, figsize=(10, 6))
# The CUSUM statistic is available in the `cusum` attribute, but usually
# it is more convenient to visually check for parameter stability using the
# `plot_cusum` method. In the plot below, the CUSUM statistic does not move
# outside of the 5% significance bands, so we fail to reject the null
def modelSm(self):
mdl = sm.RecursiveLS(self.oFrame[cols[-1]], self.oFrame[cols[:-1]]).fit()
mdl.summary()
return mdl
tickerDF[ticker].loc[:, 'maxChange'] = tickerDF[
ticker].loc[:, 'demeanCumChange'].expanding().max()
tickerDF[ticker].loc[:, 'chgRange'] = tickerDF[
ticker].loc[:, 'maxChange'] - tickerDF[ticker].loc[:, 'minChange']
tickerDF[ticker].loc[:, 'chgRatio'] = tickerDF[
ticker].loc[:, 'chgRange'] / tickerDF[ticker].loc[:, 'cumStd']
hurstMod[ticker] = RollingOLS(
np.log(tickerDF[ticker].loc[:, 'chgRatio']).dropna(),
sm.add_constant(np.log(
range(1, 1 + len(tickerDF[ticker].loc[:, 'chgRatio'].dropna()))),
prepend=False),
window=300)
recursiveHurst[ticker] = sm.RecursiveLS(
np.log(tickerDF[ticker].loc[:, 'chgRatio']).dropna(),
sm.add_constant(np.log(
range(1, 1 + len(tickerDF[ticker].loc[:, 'chgRatio'].dropna()))),
prepend=False))
res = hurstMod[ticker].fit()
res2 = recursiveHurst[ticker].fit()
res2.plot_recursive_coefficient()
#plt.figure()
#sns.regplot(np.log(range(1,1+len(tickerDF[ticker].loc[:,'chgRatio'].dropna()))), np.log(tickerDF[ticker].loc[:,'chgRatio']).dropna()).set_title(ticker)
#sns.lineplot(tickerDF[ticker].loc[:,'chgRatio'].dropna().index, res.params.loc[:,'x1'])
#plotTicker='IEF'
#sns.lineplot(x=tickerDF[ticker].index, y=tickerDF[ticker].loc[:,'chgRatio'])
#calculate and store fractional dimensions or just load them
#dimensions_long_term = scanAndStore(800, dimensionFile_long_term)
#dimensions_month = scanAndStore(30, 'dimensions_month.obj')
def RegressionAnalysis(df, Independent, Explanatory, Indicators, prefix=None):
"""
This function performs regression models, comparaison between series
Arguments:
----------
- df: Pandas DataFrame
Contains the data to be analyzed
- Independent: str
The name of column in df for the Independent variable data
- Explanatory: str or list
The name of the column in df for the Explanatory variable data. In case of a multivariate analysis, needed to pass a list object of all column names.
- Indicators: list
The list of the indicators/models names to compute
Return:
----------
- df: Pandas DataFrame
- Contains the initial df and all series indicators are added like the Residuals or the Fitted Values
- OneValueIndicators: Pandas DataFrame
- Contains all the indicators calculated with only one value like the FTest or the TTest
"""
if Indicators == None:
Indicators = [
"OLS", "GLSAR", "RecursiveLS", "Yule Walker Order 1",
"Yule Walker Order 2", "Yule Walker Order 3", "Burg Order 1",
"Burg Order 2", "Burg Order 3", "QuantReg", "GLM Binomial",
"GLM Gamma", "GLM Gaussian", "GLM Inverse Gaussian",
"GLM Negative Binomial", "GLM Poisson", "GLM Tweedie"
"AR", "ARMA", "ARIMA", "Granger Causality", "Levinson Durbin",
"Cointegration"
]
# Pre-processing
Independent = df[Independent]
Independent = pd.DataFrame(Independent)
Explanatory = df[Explanatory]
Explanatory = pd.DataFrame(Explanatory)
y_sm = np.array(Independent).reshape((-1, 1))
x_sm = np.array(Explanatory)
x_sm = sm.add_constant(x_sm)
NumDecimal = 3 # Number of decimals for rounding numbers
OneValueIndicators = {}
if prefix == None:
prefix = ""
##################################################
##### PART 1: Linear Regression
##################################################
"""
########## Section 1: OLS
"""
name = "OLS"
if name in Indicators:
name = prefix + name
model = sm.OLS(y_sm, x_sm)
results = model.fit()
### One Value Indicators
OneValueIndicators = Statsmodels_Regression_All_OneValueIndicators(
OneValueIndicators, name, results, Explanatory, NumDecimal)
### Time Series Indicators
# Fitted Values
df = Statsmodels_FittedValues(df, results, name)
# Residuals
df = Statsmodels_LR_Residuals(df, results, name)
"""
########## Section 2: WLS
"""
### Not Implemented
"""
########## Section 3: GLS
"""
### Not Implemented
"""
########## Section 4: GLSAR
"""
name = "GLSAR"
if name in Indicators:
name = prefix + name
model = sm.GLSAR(y_sm, x_sm, 1)
results = model.iterative_fit(1)
### One Value Indicators
OneValueIndicators = Statsmodels_Regression_All_OneValueIndicators(
OneValueIndicators, name, results, Explanatory, NumDecimal)
### Time Series Indicators
# Fitted Values
df = Statsmodels_FittedValues(df, results, name)
# Residuals
df = Statsmodels_LR_Residuals(df, results, name)
"""
########## Section 5: RLS
"""
name = "RecursiveLS"
if name in Indicators:
name = prefix + name
model = sm.RecursiveLS(y_sm, x_sm)
results = model.fit()
### One Value Indicators
OneValueIndicators = Statsmodels_Regression_All_OneValueIndicators(
OneValueIndicators, name, results, Explanatory, NumDecimal)
OneValueIndicators[name + " Z Value"] = results.zvalues
### Time Series Indicators
# Fitted Values
df = Statsmodels_FittedValues(df, results, name)
# Residuals
df = Statsmodels_LR_Residuals(df, results, name)
# Cumsum
# Not Implemented
"""
########## Section 6: Yule Walker ORder 1
"""
name = "Yule Walker Order 1"
if name in Indicators and len(Explanatory.columns) == 1:
name = prefix + name
rho, sigma = statsmodels.regression.linear_model.yule_walker(
x_sm[:, 1].flatten(), order=1)
### One Value Indicators
# Rho
OneValueIndicators[name + " Rho"] = round(rho[0], NumDecimal)
# Sigma
OneValueIndicators[name + " Sigma"] = round(sigma, NumDecimal)
"""
########## Section 7: Yule Walker ORder 2
"""
name = "Yule Walker Order 2"
if name in Indicators and len(Explanatory.columns) == 1:
name = prefix + name
rho, sigma = statsmodels.regression.linear_model.yule_walker(
x_sm[:, 1].flatten(), order=2)
### One Value Indicators
# Rho
OneValueIndicators[name + " Rho"] = round(rho[0], NumDecimal)
# Sigma2
OneValueIndicators[name + " Sigma"] = round(sigma, NumDecimal)
"""
########## Section 8: Yule Walker ORder 3
"""
name = "Yule Walker Order 3"
if name in Indicators and len(Explanatory.columns) == 1:
name = prefix + name
rho, sigma = statsmodels.regression.linear_model.yule_walker(
x_sm[:, 1].flatten(), order=3)
### One Value Indicators
# Rho
OneValueIndicators[name + " Rho"] = round(rho[0], NumDecimal)
# Sigma
OneValueIndicators[name + " Sigma"] = round(sigma, NumDecimal)
"""
########## Section 9: Burg's AR(p) ORder 1
"""
name = "Burg Order 1"
if name in Indicators and len(Explanatory.columns) == 1:
name = prefix + name
rho, sigma2 = statsmodels.regression.linear_model.burg(
x_sm[:, 1].flatten(), order=1)
### One Value Indicators
# Rho
OneValueIndicators[name + " Rho"] = round(rho[0], NumDecimal)
# Sigma2
OneValueIndicators[name + " Sigma2"] = round(sigma2, NumDecimal)
"""
########## Section 10: Burg's AR(p) ORder 2
"""
name = "Burg Order 2"
if name in Indicators and len(Explanatory.columns) == 1:
name = prefix + name
rho, sigma2 = statsmodels.regression.linear_model.burg(
x_sm[:, 1].flatten(), order=2)
### One Value Indicators
# Rho
OneValueIndicators[name + " Rho"] = round(rho[0], NumDecimal)
# Sigma2
OneValueIndicators[name + " Sigma2"] = round(sigma2, NumDecimal)
"""
########## Section 11: Burg's AR(p) ORder 3
"""
name = "Burg Order 3"
if name in Indicators and len(Explanatory.columns) == 1:
name = prefix + name
rho, sigma2 = statsmodels.regression.linear_model.burg(
x_sm[:, 1].flatten(), order=3)
### One Value Indicators
# Rho
OneValueIndicators[name + " Rho"] = round(rho[0], NumDecimal)
# Sigma2
OneValueIndicators[name + " Sigma2"] = round(sigma2, NumDecimal)
"""
########## Section 12: Quantile Regression
"""
name = "QuantReg"
if name in Indicators:
name = prefix + name
model = sm.QuantReg(y_sm, x_sm)
results = model.fit()
### One Value Indicators
OneValueIndicators = Statsmodels_Regression_All_OneValueIndicators(
OneValueIndicators, name, results, Explanatory, NumDecimal)
### Time Series Indicators
# Fitted Values
df = Statsmodels_FittedValues(df, results, name)
# Residuals
df = Statsmodels_LR_Residuals(df, results, name)
##################################################
##### PART 2: Generalized Linear Models
##################################################
"""
########## Section 1: GLM Binomial
"""
name = "GLM Binomial"
if name in Indicators:
name = prefix + name
model = sm.GLM(y_sm, x_sm, family=sm.families.Binomial())
results = model.fit()
### One Value Indicators
OneValueIndicators = Statsmodels_Regression_All_OneValueIndicators(
OneValueIndicators, name, results, Explanatory, NumDecimal)
OneValueIndicators["Pearson chi2"] = round(results.pearson_chi2,
NumDecimal)
### Time Series Indicators
# Fitted Values
df = Statsmodels_FittedValues(df, results, name)
# Residuals
df = Statsmodels_LR_Residuals(df, results, name)
"""
########## Section 2: GLM Gamma
"""
name = "GLM Gamma"
if name in Indicators:
name = prefix + name
model = sm.GLM(y_sm, x_sm, family=sm.families.Gamma())
results = model.fit()
### One Value Indicators
OneValueIndicators = Statsmodels_Regression_All_OneValueIndicators(
OneValueIndicators, name, results, Explanatory, NumDecimal)
OneValueIndicators["Pearson chi2"] = round(results.pearson_chi2,
NumDecimal)
### Time Series Indicators
# Fitted Values
df = Statsmodels_FittedValues(df, results, name)
# Residuals
df = Statsmodels_LR_Residuals(df, results, name)
"""
########## Section 3: GLM Gaussian
"""
name = "GLM Gaussian"
if name in Indicators:
name = prefix + name
model = sm.GLM(y_sm, x_sm, family=sm.families.Gaussian())
results = model.fit()
### One Value Indicators
OneValueIndicators = Statsmodels_Regression_All_OneValueIndicators(
OneValueIndicators, name, results, Explanatory, NumDecimal)
OneValueIndicators["Pearson chi2"] = round(results.pearson_chi2,
NumDecimal)
### Time Series Indicators
# Fitted Values
df = Statsmodels_FittedValues(df, results, name)
# Residuals
df = Statsmodels_LR_Residuals(df, results, name)
"""
########## Section 3: GLM InverseGaussian
"""
name = "GLM Inverse Gaussian"
if name in Indicators:
name = prefix + name
model = sm.GLM(y_sm, x_sm, family=sm.families.InverseGaussian())
results = model.fit()
### One Value Indicators
OneValueIndicators = Statsmodels_Regression_All_OneValueIndicators(
OneValueIndicators, name, results, Explanatory, NumDecimal)
OneValueIndicators["Pearson chi2"] = round(results.pearson_chi2,
NumDecimal)
### Time Series Indicators
# Fitted Values
df = Statsmodels_FittedValues(df, results, name)
# Residuals
df = Statsmodels_LR_Residuals(df, results, name)
"""
########## Section 4: GLM NegativeBinomial
"""
name = "GLM Negative Binomial"
if name in Indicators:
name = prefix + name
model = sm.GLM(y_sm, x_sm, family=sm.families.NegativeBinomial())
results = model.fit()
### One Value Indicators
OneValueIndicators = Statsmodels_Regression_All_OneValueIndicators(
OneValueIndicators, name, results, Explanatory, NumDecimal)
OneValueIndicators["Pearson chi2"] = round(results.pearson_chi2,
NumDecimal)
### Time Series Indicators
# Fitted Values
df = Statsmodels_FittedValues(df, results, name)
# Residuals
df = Statsmodels_LR_Residuals(df, results, name)
"""
########## Section 5: GLM Poisson
"""
name = "GLM Poisson"
if name in Indicators:
name = prefix + name
model = sm.GLM(y_sm, x_sm, family=sm.families.Poisson())
results = model.fit()
### One Value Indicators
OneValueIndicators = Statsmodels_Regression_All_OneValueIndicators(
OneValueIndicators, name, results, Explanatory, NumDecimal)
OneValueIndicators["Pearson chi2"] = round(results.pearson_chi2,
NumDecimal)
### Time Series Indicators
# Fitted Values
df = Statsmodels_FittedValues(df, results, name)
# Residuals
df = Statsmodels_LR_Residuals(df, results, name)
"""
########## Section 6: GLM Tweedie
"""
name = "GLM Tweedie"
if name in Indicators:
name = prefix + name
model = sm.GLM(y_sm, x_sm, family=sm.families.Tweedie())
results = model.fit()
### One Value Indicators
OneValueIndicators = Statsmodels_Regression_All_OneValueIndicators(
OneValueIndicators, name, results, Explanatory, NumDecimal)
OneValueIndicators["Pearson chi2"] = round(results.pearson_chi2,
NumDecimal)
### Time Series Indicators
# Fitted Values
df = Statsmodels_FittedValues(df, results, name)
# Residuals
df = Statsmodels_LR_Residuals(df, results, name)
##################################################
##### PART 3: Robust Linear Models
##################################################
##################################################
##### PART 4: AR models
##################################################
name = "AR"
if name in Indicators:
name = prefix + name
model = statsmodels.tsa.ar_model.AR(Independent)
results = model.fit()
### One Value Indicators
OneValueIndicators = Statsmodels_Regression_All_OneValueIndicators(
OneValueIndicators, name, results, Explanatory, NumDecimal)
OneValueIndicators[name + " Final Prediction Error"] = results.fpe
OneValueIndicators[
name + " Hannan-Quinn Information Criterion"] = results.hqic
OneValueIndicators[name + " Roots"] = results.roots
### Time Series Indicators
# Fitted Values
df = Statsmodels_FittedValues(df, results, name)
# Residuals
df = Statsmodels_LR_Residuals(df, results, name)
##################################################
##### PART 5: ARMA
##################################################
name = "ARMA"
if name in Indicators:
name = prefix + name
model = statsmodels.tsa.arima_model.ARMA(y_sm, (5, 5), x_sm)
results = model.fit()
### One Value Indicators
OneValueIndicators = Statsmodels_Regression_All_OneValueIndicators(
OneValueIndicators, name, results, Explanatory, NumDecimal)
OneValueIndicators[name + " AR Params"] = results.arparams
OneValueIndicators[name + " AR Roots"] = results.arroots
OneValueIndicators[name + " AR Freq"] = results.arfreq
OneValueIndicators[
name + " Hannan-Quinn Information Criterion"] = results.hqic
OneValueIndicators[name + " MA Params"] = results.maparams
try:
OneValueIndicators[name + " MA Roots"] = results.maroots
except:
pass
try:
OneValueIndicators[name + " MA Freq"] = results.mafreq
except:
pass
OneValueIndicators[name + " Sigma2"] = results.sigma2
### Time Series Indicators
# Fitted Values
df = Statsmodels_FittedValues(df, results, name)
# Residuals
df = Statsmodels_LR_Residuals(df, results, name)
##################################################
##### PART 6: ARIMA
##################################################
name = "ARIMA"
if name in Indicators:
name = prefix + name
model = statsmodels.tsa.arima_model.ARIMA(Independent, (2, 2, 2),
Explanatory)
results = model.fit()
### One Value Indicators
OneValueIndicators = Statsmodels_Regression_All_OneValueIndicators(
OneValueIndicators, name, results, Explanatory, NumDecimal)
OneValueIndicators[name + " AR Params"] = results.arparams
OneValueIndicators[name + " AR Roots"] = results.arroots
OneValueIndicators[name + " AR Freq"] = results.arfreq
OneValueIndicators[
name + " Hannan-Quinn Information Criterion"] = results.hqic
OneValueIndicators[name + " MA Params"] = results.maparams
OneValueIndicators[name + " MA Roots"] = results.maroots
OneValueIndicators[name + " MA Freq"] = results.mafreq
OneValueIndicators[name + " Sigma2"] = results.sigma2
### Time Series Indicators
# Fitted Values
df = Statsmodels_FittedValues(df, results, name)
# Residuals
df = Statsmodels_LR_Residuals(df, results, name)
##################################################
##### PART 7: Univariate Analysis
##################################################
# Granger Causality
name = "Granger Causality"
name = prefix + name
if name in Indicators:
OneValueIndicators[name] = ts.grangercausalitytests(
Independent.merge(Explanatory,
how="inner",
left_index=True,
right_index=True),
maxlag=10)
# Levinson Durbin
name = "Levinson Durbin"
name = prefix + name
if name in Indicators:
OneValueIndicators[name] = ts.levinson_durbin(Independent)
# Cointegration
name = "Cointegration"
name = prefix + name
if name in Indicators:
OneValueIndicators[name] = ts.coint(Independent,
Explanatory,
trend="ct",
return_results=False)
##################################################
##### Not Implemented
##################################################
# BDS Statistic (residuals analysis)
# Not Implemented
# Return’s Ljung-Box Q Statistic (AR)
# Not Implemented
OneValueIndicators = pd.DataFrame.from_dict(OneValueIndicators,
orient="index")
return df, OneValueIndicators
def ssri(i, n, x, y, k):
# ret=recresid(x[i-1:],y[i-1:])
mod = sm.RecursiveLS(x[i:], y[i:])
ret = mod.fit().cusum_squares
return np.concatenate((np.array([np.nan] * k), ret))
row[0] = (row[0] - temp_min) / (temp_max - temp_min)
#model = Sequential()
#model.add(Dense(4,input_dim=1,activation='relu'))
#model.add(Dense(4,activation='relu'))
#model.add(Dense(1,activation='sigmoid'))
#model.compile(loss='mean_squared_error',optimizer='SGD',metrics=['mean_squared_error'])
# history=model.fit(temp,pressure,epochs=10,verbose=1)
#scores = model.evaluate(temp,pressure)
#print("Baseline Error: %.2f%%" % (100-scores[1]*100))
#predictions = model.predict(temp)
#plt.plot(predictions,'r')
#plt.plot(history.history['loss'])
mod = sm.RecursiveLS(temp, pressure)
res = mod.fit()
print(res.summary())
plt.plot(temp, 'b')
plt.plot(pressure, 'g')
plt.legend(['Prediction', 'Temp', 'Pressure'])
plt.show()
except (Exception, psycopg2.Error) as error:
print("Error ", error)
finally:
if (connection):
cursor.close()
connection.close()
print("Postgresql connection is closed")
def fn_apis_statsmodels_recursive_ls():
import numpy as np
x = request.args.get('x')
y = request.args.get('y')
x1 = np.array(eval(x))
y1 = np.array(eval(y))
#x1 = [[4,67,662],[9,19,618],[6,49,372],[6,33,58],[1,18,153],[2,78,938],[3,15,627],[8,55,191],[2,47,812],[2,83,946],[2,4,895],[9,37,42],[0,1,595],[7,27,392],[5,22,836],[0,12,513],[2,41,601],[3,68,615],[2,23,649],[1,98,9],[9,40,32],[5,77,798],[1,10,903],[1,53,772],[7,20,716],[2,35,678],[5,52,258],[7,31,814],[2,30,577]]
#y1 = [2857.0163,2547.5962,1647.6061,343.8966,668.2108,3990.0414,2559.0662,945.1439,3393.1068,4037.1068,3596.0458,297.5798,2383.6193,1663.8839,3420.5135,2088.0197,2531.2703,2670.7878,2669.8044,332.9981,266.718,3433.975,3644.3636,3249.3518,2938.0325,2821.3308,1198.4373,3363.5752,2402.6042]
x1 = sm.add_constant(x1)
model = sm.RecursiveLS(y1, x1)
rs = model.fit()
c = rs_ols(
rs.aic,
rs.bic,
rs.bse.tolist(),
rs.conf_int().tolist(),
rs.cov_kwds,
rs.cov_params().tolist(),
rs.cov_params_approx.tolist(),
rs.cov_params_default.tolist(),
rs.cov_params_oim.tolist(),
rs.cov_params_opg.tolist(),
rs.cov_params_robust.tolist(),
rs.cov_params_robust_approx.tolist(),
rs.cov_params_robust_oim.tolist(),
rs.cov_type,
rs.cusum.tolist(),
rs.cusum_squares.tolist(),
rs.data_in_cache,
rs.df_resid,
rs.filtered_state.tolist(),
rs.filtered_state_cov.tolist(),
rs.fittedvalues.tolist(),
rs.forecasts.tolist(),
rs.forecasts_error.tolist(),
rs.forecasts_error_cov.tolist(),
rs.hqic,
rs.k_constant,
rs.llf,
rs.llf_obs.tolist(),
rs.loglikelihood_burn,
rs.nobs,
rs.params.tolist(),
rs.predict().tolist(),
rs.predicted_state.tolist(),
rs.predicted_state_cov.tolist(),
rs.pvalues.tolist(),
rs.resid.tolist(),
rs.resid_recursive.tolist(),
rs.scale,
rs.smoothed_measurement_disturbance.tolist(),
rs.smoothed_measurement_disturbance_cov.tolist(),
rs.smoothed_state.tolist(),
rs.smoothed_state_cov.tolist(),
rs.smoothed_state_disturbance.tolist(),
rs.smoothed_state_disturbance_cov.tolist(),
rs.tvalues.tolist(),
rs.use_t,
rs.zvalues.tolist(),
)
c = c.__dict__
tmp = json.dumps(c, ensure_ascii=False, indent=4)
return Response(tmp,
mimetype='application/json',
headers={
"Access-Control-Allow-Origin": "http://127.0.0.0:5000",
"Access-Control-Allow-Methods": "GET",
"Access-Control-Allow-Headers":
"x-requested-with,content-type",
"Access-Control-Allow-Credentials": "true"
})
