# model = "AC"
# model = "KF"
if model.lower() == 'kf':
p = 25
kf = KalmanFilter(data_dict, p)
kf.fit()
kf.display(out="./co2_kf.png")
if model.lower() == "ar":
p = 50
my_ar = AutoRegressive(data_dict, p)
my_ar.fit()
my_ar.predict()
my_ar.display(out="./co2_ar.png")
if model.lower() == "ac":
p = 50
my_ac = AutoCorrelation(data_dict, p)
my_ac.fit()
my_ac.predict()
my_ac.display(out="./co2_ac.png")
my_ac.spectrum()
if model.lower() == "gp":
Q = 3
use_kernels = "exponential_quadratic* cosine"
for _ in range(Q - 1):
# model = "AR"
model = "AC"
# model = "KF"
if model.lower() == 'kf':
p = 10
kf = KalmanFilter(data_dict, p)
kf.fit()
kf.display(out="./mg_kf.png")
if model.lower() == "ar":
p = 50
my_ar = AutoRegressive(data_dict, p)
my_ar.fit()
my_ar.predict()
my_ar.display(out="./mg_ar.png")
if model.lower() == "ac":
p = 50
my_ac = AutoCorrelation(data_dict, p)
my_ac.fit()
my_ac.predict()
my_ac.display(out="./mg_ac.png")
my_ac.spectrum()
my_ac.plot_attr("spectrum", show=True)
if model.lower() == "gp":
Q = 3
use_kernels = "exponential_quadratic* cosine"
# model = "AR"
# model = "AC"
if model.lower() == 'kf':
p = 100
kf = KalmanFilter(data_dict, p)
kf.fit()
kf.display(out="./sun_kf.png")
if model.lower() == "ar":
p = 50
my_ar = AutoRegressive(data_dict, p)
my_ar.fit()
my_ar.predict()
my_ar.display(out="./sun_ar.png")
if model.lower() == "ac":
p = 50
my_ac = AutoCorrelation(data_dict, p)
my_ac.fit()
my_ac.predict()
my_ac.display(out="./sun_ac.png")
my_ac.spectrum()
if model.lower() == "gp":
# Q = 3
# use_kernels = "exponential_quadratic* cosine"
# for _ in range(Q - 1):
model = "AR"
model = "AC"
# model = "KF"
if model.lower() == 'kf':
p = 10
kf = KalmanFilter(data_dict, p)
kf.fit()
kf.display(out="./fin_kf.png")
if model.lower() == "ar":
p = 50
my_ar = AutoRegressive(data_dict, p)
my_ar.fit()
my_ar.predict()
my_ar.display(out="./fin_ar.png")
if model.lower() == "ac":
p = 50
my_ac = AutoCorrelation(data_dict, p)
my_ac.fit()
my_ac.predict()
my_ac.display(out="./fin_ac.png")
my_ac.spectrum()
if model.lower() == "gp":
Q = 3
use_kernels = "exponential_quadratic* cosine"
for _ in range(Q - 1):
