使用pykalman预测动态对象上的进一步步骤

pip install pykalman

import numpy as np
from pykalman import KalmanFilter
import matplotlib.pyplot as plt

kf = KalmanFilter(transition_matrices=[1],
                  observation_matrices=[1],
                  initial_state_mean=0,
                  initial_state_covariance=1,
                  observation_covariance=1,
                  transition_covariance=0.01)

# 假设你有一个名为 data 的 NumPy 数组，其中包含你的时间序列数据
states_pred, _ = kf.filter(data)

# 假设你想预测接下来的 10 个时间步
predictions = []
last_state = states_pred[-1]

for _ in range(10):
    last_state, _ = kf.filter_update(last_state, None)
    predictions.append(last_state[0])

plt.plot(data, label='Original Data')
plt.plot(states_pred, label='Filtered Data')
plt.plot(range(len(data), len(data) + len(predictions)), predictions, label='Predictions')
plt.legend()
plt.show()