def __init__(self):
self.results = results[results[0] == 4]
self.bds_stats = np.array(self.results[2][1:])
self.pvalues = np.array(self.results[3][1:])
self.data = data[3][data[3].notnull()]
self.res = bds(self.data, 5)
def __init__(self):
self.results = results[results[0] == 4]
self.bds_stats = np.array(self.results[2][1:])
self.pvalues = np.array(self.results[3][1:])
self.data = data[3][data[3].notnull()]
self.res = bds(self.data, 5)
def setup_class(cls):
cls.results = results[results[0] == 3]
cls.bds_stats = np.array(cls.results[2].iloc[1:])
cls.pvalues = np.array(cls.results[3].iloc[1:])
cls.data = data[2][data[2].notnull()]
cls.res = bds(cls.data, 5)
def setup_class(cls):
cls.results = results[results[0] == 3]
cls.bds_stats = np.array(cls.results[2][1:])
cls.pvalues = np.array(cls.results[3][1:])
cls.data = data[2][data[2].notnull()]
cls.res = bds(cls.data, 5)
def independence(
other_args: List[str],
residuals: List[float],
):
"""Tests independent and identically distributed (i.i.d.) time series (BDS)
Parameters
----------
other_args : str
Command line arguments to be processed with argparse
residuals : List[float]
Residuals data
"""
parser = argparse.ArgumentParser(
add_help=False,
prog="independence",
description="""
Tests independent and identically distributed (i.i.d.) time series (BDS)
""",
)
try:
ns_parser = parse_known_args_and_warn(parser, other_args)
if not ns_parser:
return
result = bds(residuals, max_dim=6)
print("BDS Test")
print("Dim 2: z-static {:.4f} Prob {:.4f}".format(
result[0][0], result[1][0]))
print("Dim 3: z-static {:.4f} Prob {:.4f}".format(
result[0][1], result[1][1]))
print("Dim 4: z-static {:.4f} Prob {:.4f}".format(
result[0][2], result[1][2]))
print("Dim 5: z-static {:.4f} Prob {:.4f}".format(
result[0][3], result[1][3]))
print("Dim 6: z-static {:.4f} Prob {:.4f}".format(
result[0][4], result[1][4]))
print("")
except Exception as e:
print(e, "\n")
return
from statsmodels.tsa.stattools import bds
from rcnn.dataloader import CNNDataLoader
import warnings
warnings.filterwarnings("ignore")
if __name__ == '__main__':
regression_datasets = [
"BenzeneConcentration", "HouseholdPowerConsumption1",
"NewsHeadlineSentiment", "BIDMC32HR", "LiveFuelMoistureContent",
"IEEEPPG", "PPGDalia"
]
for name in regression_datasets:
print(name)
dataloader = CNNDataLoader(
path_to_file="/path/to/Monash_UEA_UCR_Regression_Archive/"
f"{name}/{name}_TEST.ts",
img_size=4)
residuals = dataloader.get_residuals()
for i in range(residuals.shape[0]):
_, pval = bds(residuals[i])
print(pval)
#obtain final residual series by refitting the model with best parameter combination:
am = arch_model(u_hat,
mean='Zero',
vol='GARCH',
p=garch_order[0],
q=garch_order[1],
rescale=False).fit(disp='off')
u_hat_std = u_hat / np.sqrt(am.conditional_volatility)
#BDS Test, epsilon is chosen according to
epsilon = 0.5 * np.std(u_hat_std)
max_dim = 5
bdsstat = bds(np.log(u_hat_std**2), max_dim=max_dim, epsilon=epsilon)[1]
df = pd.DataFrame()
df['m'] = [str(i) for i in range(2, max_dim + 1)]
df['p-value'] = bdsstat
caption = 'BDS Test Results'
df_str = df.T.to_latex(caption=caption, header=False)
#split latex string and insert reference
str_tmp = re.split(r'(aption{' + caption + r'})', df_str)
str_out = str_tmp[0] + str_tmp[
1] + '\n' + r'\label{tab:' + 'bds' + r'}' + str_tmp[2]
def bds(self):
result = stattools.bds(self.timeseries)
BdsResult = namedtuple('BdsResult', 'statistic pvalue')
return BdsResult(result[0], result[1])
file.write("mean: " + str(numpy.mean(array)) + "\n")
file.write("variance: " + str(numpy.var(array)) + "\n")
file.write("standard deviation: " +
str(statistics.stdev(array)) + "\n")
kpss_stat, p_value, lags, crit = stat.kpss(
array
) # https://www.statsmodels.org/dev/generated/statsmodels.tsa.stattools.kpss.html
file.write("KPSS: " + str(kpss_stat) + "\n")
start = 0
end = 200
max_bds = 0
while (end <= len(array)
): # sliding window with fixed size (200 elements)
bds_stat, pvalue = stat.bds(
array[start:end]
) # https://www.statsmodels.org/dev/generated/statsmodels.tsa.stattools.bds.html
if (bds_stat > max_bds):
max_bds = bds_stat
start += 1
end += 1
file.write("BDS: " + str(max_bds) + "\n")
H, c, data = compute_Hc(
array, kind='change',
simplified=True) # https://pypi.org/project/hurst/
file.write("Hurst Exponent: " + str(H) + "\n")
file.close()
def conv_to_float(n):
if type(n) == int or type(n) == float:
return float(n)
l = n.split('.')
if (len(l) == 1):
return float(l[0])
m = l[1] + "." + l[-2]
return float(m)
df = pd.read_csv("merge_oil.csv")
#BDS Test over columns
us_price = [conv_to_float(i) for i in list(df["US_Price"])]
print(stattools.bds(us_price, max_dim=2, epsilon=None, distance=1.5))
dollar_eq = [conv_to_float(i) for i in list(df["Dollar_eq"])]
print("Dollar_eq")
print(stattools.bds(dollar_eq, max_dim=2, epsilon=None, distance=1.5))
price = [float(i) for i in df["Price"]]
print("Price")
print(stattools.bds(price, max_dim=2, epsilon=None, distance=1.5))
Open = [float(i) for i in df["Open"]]
print("Open")
print(stattools.bds(Open, max_dim=2, epsilon=None, distance=1.5))
high = [float(i) for i in df["High"]]
print("High")
