def time_series_standard_deviation(x):
"""
:param x: the time series to calculate the feature of
:type x: pandas.Series
:return: the value of this feature
:return type: float
"""
return ts_feature_calculators.standard_deviation(x)
def TS_features12(signal):
stand_deviation = ts.standard_deviation(signal)
sum_reoccurring = ts.sum_of_reoccurring_data_points(signal)
sum_r_value = ts.sum_of_reoccurring_values(signal)
sum_v = ts.sum_values(signal)
variance = ts.variance(signal)
variance_larger_than_sd = ts.variance_larger_than_standard_deviation(
signal)
return stand_deviation, sum_reoccurring, sum_r_value, sum_v, variance, variance_larger_than_sd
def extract_feats(ts):
std = fc.standard_deviation(ts)
kurtosis = fc.kurtosis(ts)
skewness = fc.skewness(ts)
cam = fc.count_above_mean(ts)
cbm = fc.count_below_mean(ts)
lsam = fc.longest_strike_above_mean(ts)
lsbm = fc.longest_strike_below_mean(ts)
psd = fc.fourier_entropy(ts, bins=1000000)
energy = fc.abs_energy(ts)
return np.array(
[std, kurtosis, skewness, cam, cbm, lsam, lsbm, psd, energy])
def extract_features(data):
day = 24 * 60
return list(
numpy.nan_to_num(
numpy.array([
feature.symmetry_looking(data, [{
'r': 0.3
}])[0][1],
feature.variance_larger_than_standard_deviation(data).bool(),
feature.ratio_beyond_r_sigma(data, 2),
feature.has_duplicate_max(data),
feature.has_duplicate_min(data),
feature.has_duplicate(data),
feature.agg_autocorrelation(numpy.array(data.value),
[{
'f_agg': 'mean',
'maxlag': day
}])[0][1],
feature.partial_autocorrelation(data, [{
'lag': day
}])[0][1],
feature.abs_energy(numpy.array(data.value)),
feature.mean_change(data),
feature.mean_second_derivative_central(data),
feature.median(data),
float(feature.mean(data)),
float(feature.standard_deviation(data)),
float(feature.longest_strike_below_mean(data)),
float(feature.longest_strike_above_mean(data)),
int(feature.number_peaks(data, 10)),
feature.linear_trend(numpy.array(data.value), [{
'attr': 'rvalue'
}])[0][1],
feature.c3(data, day),
float(feature.maximum(data)),
float(feature.minimum(data))
])))
def get_sd(arr):
res = np.array([standard_deviation(arr)])
res = np.nan_to_num(res)
return res
