def test_estimate_trend():
x = np.arange(0, 100)
y = np.arange(0, 100)
noise = np.random.normal(size=100)
signal = y + noise
estimation = HPfilter()
cycle, trend = estimation.estimate_trend(x, signal)
assert np.sum(y-trend)/y.shape[0] < 0.5
# %%
import matplotlib.pyplot as plt
from src.compare_functions import trend_estimation_comparison
from src.data_handler import data_handler
from src.methods.dws import DWS
from src.methods.emd import EmpiricalModeDecomposition
from src.methods.lowess import Lowess
from src.methods.hp_filter import HPfilter
from src.methods.ita import ITA
from src.methods.mk import MannKendall
from src.methods.regression import Regression
from src.methods.splines import Splines
from src.methods.theil import Theil
from src.methods.regression import Regression
if __name__ == '__main__':
methods_detection = [ITA(), MannKendall(), Regression(), Theil()]
methods_estimation = [DWS(), HPfilter(), Splines(), Theil(), Lowess(), EmpiricalModeDecomposition(), Regression()]
x, y, trend, seasonality, noise = data_handler.generate_synthetic_data('data', 'monthly.ini')
plt.plot(x, y)
plt.plot(x, trend)
plt.show()
trend_estimation_comparison(methods_estimation, 'monthly')
def obtain_config(config_file_name: str) -> configparser.ConfigParser:
"""
Read the config file
:param config_file_name: name of the file
:return: parameters in the config file
"""
config = configparser.ConfigParser(allow_no_value=True)
config_file_path = SYNTHETIC_DIR + '/' + config_file_name
config.read(config_file_path)
return config
if __name__ == '__main__':
generate_data()
methods_detection = [ITA(), MannKendall(), Regression(), Theil()]
methods_estimation = [
EmpiricalModeDecomposition(),
HPfilter(),
Splines(),
Theil(),
Regression(),
Lowess()
]
trend_estimation_comparison(methods_estimation, '*')
#generate_with_name('func', 5)
from src.utils import generate_timestamp
def show_estimation(methods: List[Method], time_series_x: np.ndarray,
time_series_y: np.ndarray) -> None:
for method in methods:
trend_estimation = method.estimate_trend(time_series_x, time_series_y)
plt.figure(figsize=(12, 8))
plt.plot(time_series_x, time_series_y)
plt.plot(time_series_x, trend_estimation, label='Estimated trend')
plt.title(f'Trend estimation of {method.name}')
plt.xlabel('Days from January 1962 to July 2012')
plt.ylabel('Difference between LOD and 86400 seconds (ms)')
plt.savefig(f'{PLOTS_DIR}/case_study_{generate_timestamp()}.png')
plt.show()
plt.close()
if __name__ == '__main__':
methods_detection = [MannKendall(), ITA(plot=True)]
methods_estimation = [Regression(), Lowess(), Splines(), HPfilter(), DWS()]
data = loadmat(f'{DATA_DIR}/case_study.mat')
y = data['LOD'].squeeze()
x = np.arange(0, y.shape[0])
#print(method_detection.detect_trend(x, y))
show_estimation(methods_estimation, x, y)
f'snr: {noise_title}')
for method in methods_list:
estimation = method.estimate_trend(x, y)
plt.plot(x, estimation, label=method.name)
distance = np.linalg.norm((estimation - trend))
file_results.append(distance)
plt.plot(x, trend, label='True trend', linewidth=3.0, color='k', linestyle=':')
plt.legend()
plt.savefig(f'{PLOTS_DIR}/{name}_{timestamp}.png')
plt.close()
results[name] = file_results
table = pd.DataFrame(results, columns)
table.to_csv(f'{RESULTS_DIR}/trend_estimation_{file_prefix}_{timestamp}.csv')
return table
if __name__ == '__main__':
# methods_detection = [MannKendall(), ITA(), Theil(), DWS(), EmpiricalModeDecomposition(),
# HPfilter(), Splines()]
#
# trend_detection_comparison(methods_detection, 'func')
methods_estimation = [Theil(), DWS(), EmpiricalModeDecomposition(), HPfilter(), Splines()]
trend_estimation_comparison(methods_estimation, 'func')