部分代码: kft = KalmanFilter( A,C,Q,R,B,D,m0,P0, random_state=random_state )# model should be state, observation = kft.sample( n_timesteps=step, initial_state=m0 )# provide data #filtered_state_estimatet, f_covt = kft.filter(observation) #smoothed_state_estimatet, s_covt = kft...
observation = kft.sample(n_timesteps=step,initial_state=m0)# provide data#filtered_state_estimatet, f_covt = kft.filter(observation)#smoothed_state_estimatet, s_covt = kft.smooth(observation)'''Step 2: Initialize our model'''# specify parameterstransition_matrix = Atransition_offset...
针对电动汽车（Electric Vehicle,EV)充电负荷序列较强的随机性和间歇性而导致预测准确度不高的问题，利用双向长短期记忆网络（Bi-Long Short-Term Memory,Bi-LSTM)善于处理时间序列数据的特点，提出一种基于Bi-LSTM的EV充电负荷预测方法。验证了基 于Bi-LSTM的EV预测模型比LSTM和ARMA预测模型具有更高的准确性，可为...
部分代码: kft = KalmanFilter( A,C,Q,R,B,D,m0,P0, random_state=random_state )# model should be state, observation = kft.sample( n_timesteps=step, initial_state=m0 )# provide data #filtered_state_estimatet, f_covt = kft.filter(observation) #smoothed_state_estimatet, s_covt = kft...
#filtered_state_estimatet, f_covt = kft.filter(observation) #smoothed_state_estimatet, s_covt = kft.smooth(observation) ''' Step 2: Initialize our model ''' # specify parameters transition_matrix = A transition_offset = B observation_matrix = C ...