# 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):