Wonder if Kalman filter fits imputing with lots of missing

[XiaoJia849]

i use Kalman filter to impute missing value in the time series, in the pic, the origin curve is the original curve, the red zone is missing, and i use UnscentedKalmanFilter to do the imputation, which generates the blue parts.

Obviously, in small missing area, the filter fits great, while in the big missing area, the imputation seems work bad. I wonder if Kalman filter fits imputing with lots of missing, or maybe i change some hyperparameters can fix this problem.

Here are my codes, part of them are copied from filterpy.kalman.UKF.UnscentedKalmanFilter. Thank you very very very very very much !!!!!!

def init_filter(fx,hx,initial_x):
    dt = 0.2
    # create sigma points to use in the filter. This is standard for Gaussian processes
    points = MerweScaledSigmaPoints(3, alpha=.1, beta=2., kappa=0)
    kf = UnscentedKalmanFilter(dim_x=3, dim_z=1, dt=dt, fx=fx, hx=hx, points=points)
    kf.x = np.array([initial_x, 0,0]) # initial state
    kf.P *= 0.2 # initial uncertainty
    z_std = 0.1 
    kf.R = np.diag([z_std**2]) 
    kf.Q = Q_discrete_white_noise(dim=3, dt=dt, var=0.01**2, block_size=1)
    return kf

def fx(x, dt):
    F = np.array([[1, dt,.5*dt*dt],
                  [0, 1,dt],
                  [0.,0.,1]], dtype=float)
    return np.dot(F, x)
def hx(x):
    # measurement function - convert state into a measurement
    # where measurements are [x_pos, y_pos]
    return np.array([x[0]])
    

def process_one_flux(fx,hx,input_flux):
    input_flux=input_flux.copy()
    idxs=np.argwhere(np.isnan(input_flux)).flatten()
    diff_idxs=np.diff(idxs)
    start_i,_=idxs[np.argmax(diff_idxs)]+1,idxs[np.argmax(diff_idxs)+1]
    initial_x=input_flux[start_i]
    kf=init_filter(fx,hx,initial_x)
    outs=[] 
    zs=input_flux.reshape((-1,1))[start_i:].tolist()
    for z in zs:
        if np.isnan(z):
            kf.predict()
            kf.update([kf.x[0]])
            outs.append(kf.x[0])
        else:
            kf.predict()
            kf.update(z)
            outs.append(kf.x[0])
    outs=np.array([np.nan]*(start_i)+outs)
    mask=np.isnan(input_flux)
    input_flux[mask]=outs[mask]
    if start_i>0:
        initial_x=input_flux[-1]
        # print("initial_x",initial_x)
        kf=init_filter(fx,hx,initial_x)
        outs=[] 
        zs=input_flux.reshape((-1,1)).tolist()
        for z in zs[::-1]:
            if np.isnan(z):
                kf.predict()
                # kf.update([kf.x[0]])
                outs.append(kf.x[0])
            else:
                kf.predict()
                kf.update(z)
                outs.append(kf.x[0])
        outs=np.array(outs[::-1])
        mask=np.isnan(input_flux)
        input_flux[mask]=outs[mask]
    return input_flux
    

[XiaoJia849]

for up pic, in fact many small missing zone didn't successfully show, the pic next show all the missing zones: