def time_lagged_correlation():
"""Runs the time-lagged correlation analysis experiment.
This function alculates the time-lagged correlation between the variables for lags of up to 48 hours and plots the
result as a scatterplot matrix.
"""
var_names = [
"dayOfYear", "minuteOfYear", "minuteOfDay", "dayOfWeek", "isWeekend",
"humidity_sensor", "temperature", "precip_intensity", "cloud_cover",
"p1", "p2", "dew_point", "wind_speed"
]
tau_min = 0
tau_max = 48
dataframe, var_list = generate_dataframe(var_names)
print(f"Variable names: {var_names}")
ci_test = ParCorr(significance='analytic')
pcmci = PCMCI(dataframe=dataframe, cond_ind_test=ci_test, verbosity=1)
correlations = pcmci.get_lagged_dependencies(tau_min=tau_min,
tau_max=tau_max)
lag_func_matrix = tp.plot_lagfuncs(
name="experiments/causal_discovery/results/time_lagged_correlation.png",
val_matrix=correlations,
setup_args={
'var_names': var_names,
'figsize': (50, 25),
'label_fontsize': 12,
'label_space_top': 0.025,
'label_space_left': 0.05,
'lag_units': 'hours',
'x_base': 6,
'y_base': .5
})
print(lag_func_matrix)
#var_names = [r'$X^0$', r'$X^1$', r'$X^2$', r'$X^3$']
dataframe = pp.DataFrame(data,
datatime=np.arange(len(data)),
var_names=headers)
if verbose > 0:
plot = tp.plot_timeseries(dataframe)[0]
if display_images:
plot.show()
if save_images:
plot.savefig("timeseries.png")
parcorr = ParCorr(significance='analytic')
pcmci = PCMCI(dataframe=dataframe, cond_ind_test=parcorr, verbosity=1)
correlations = pcmci.get_lagged_dependencies(tau_max=3)
lag_func_matrix = tp.plot_lagfuncs(val_matrix=correlations,
setup_args={
'var_names': headers,
'x_base': 5,
'y_base': .5
})
if verbose > 1:
if display_images:
lag_func_matrix.savefig()
if save_images:
lag_func_matrix.savefig("lag_func.png")
pcmci.verbosity = 1
results = pcmci.run_pcmci(tau_max=tau_max, pc_alpha=None)
data.shape
# In[5]:
tp.plot_timeseries(dataframe)
plt.show()
# In[6]:
parcorr = ParCorr(significance='analytic')
pcmci = PCMCI(dataframe=dataframe, cond_ind_test=parcorr, verbosity=1)
# In[7]:
correlations = pcmci.get_lagged_dependencies(tau_max=20,
val_only=True)['val_matrix']
lag_func_matrix = tp.plot_lagfuncs(val_matrix=correlations,
setup_args={
'figsize': (6, 6),
'var_names': var_names,
'x_base': 10,
'y_base': .5
})
# In[8]:
pcmci.verbosity = 0
results = pcmci.run_pcmci(tau_max=8, pc_alpha=None)
# In[9]:
