D = np.array([[0, 0]])
Q = np.array([[0.1, 0],
[0, 0.1]])
R = np.array([[0.1]])
params = (A, B, C, D, Q, R)
# Initial Values
init_mu = np.array([[1],
[2]])
init_V = np.array([[INIT_VAR, 0],[0, INIT_VAR]])
init_state = (init_mu, init_V)
# Data Generator
space_gen = KalmanSpaceGenerator(params, init_mu, init_V, 100)
y_0tT, u_0tT = space_gen.generate_data()
# Kalman Filter
kf = KalmanFilter(params, init_mu, init_V)
# Assign data
(ys, us) = line_gen(100)
kf.ys = y_0tT
kf.us = u_0tT
y_pred, ll, s = kf.smooth_filter()
l1, = plt.plot(y_0tT.flatten(), label='Kalman Space', color='blue')
l2, = plt.plot(y_pred.flatten(), label='Online Predictions', color='green')
l3, = plt.plot(y_0tT.flatten() - y_pred.flatten(), label='Error', color='red')
plt.legend(handles=[l1, l2])
plt.show()
[0, 0, 0, 0, 0], [0, 0, 0, 0, 1]])
R = np.array([[1, 0], [0, 1]])
init_mu = np.array([[0], [5], [0], [0], [0]])
init_V = np.array(
[[100000000000, 100000000000, 100000000000, 100000000000, 100000000000],
[100000000000, 100000000000, 100000000000, 100000000000, 100000000000],
[100000000000, 100000000000, 100000000000, 100000000000, 100000000000],
[100000000000, 100000000000, 100000000000, 100000000000, 100000000000],
[100000000000, 100000000000, 100000000000, 100000000000, 100000000000]])
kf = KalmanFilter((A, B, C, D, Q, R), init_mu, init_V)
kf.ys = obs
kf.us = conds
(y_pred, ll, _) = kf.smooth_filter()
(x, y, t) = y_pred.shape
pred = pd.DataFrame(y_pred.reshape((x, t)).T,
columns=['Predicted Liquidity', 'Predicted Prices'])
# Projected prices
# (projection, V_projections) = kf.project(30)
# plt.plot(projection[1, :, :].flatten(), c='r')
# plt.show()
# Ordinary Least Squares Calculation
ols = sum((rmd.data.values[:, 2] - pred.values[:, 1]) *
(rmd.data.values[:, 2] - pred.values[:, 1])) / 2
print(ols)
# Plot results
B = np.array([[0, 0], [0, 0]]) C = np.array([[1, 0]]) D = np.array([[0, 0]]) Q = np.array([[0.1, 0], [0, 0.1]]) R = np.array([[0.1]]) params = (A, B, C, D, Q, R) # Initial Values init_mu = np.array([[1], [2]]) init_V = np.array([[INIT_VAR, 0], [0, INIT_VAR]]) init_state = (init_mu, init_V) # Data Generator space_gen = KalmanSpaceGenerator(params, init_mu, init_V, 100) y_0tT, u_0tT = space_gen.generate_data() # Kalman Filter kf = KalmanFilter(params, init_mu, init_V) # Assign data (ys, us) = line_gen(100) kf.ys = y_0tT kf.us = u_0tT y_pred, ll, s = kf.smooth_filter() l1, = plt.plot(y_0tT.flatten(), label='Kalman Space', color='blue') l2, = plt.plot(y_pred.flatten(), label='Online Predictions', color='green') l3, = plt.plot(y_0tT.flatten() - y_pred.flatten(), label='Error', color='red') plt.legend(handles=[l1, l2]) plt.show()
init_mu = np.array([[0],
[5],
[0],
[0],
[0]])
init_V = np.array([[100000000000, 100000000000, 100000000000, 100000000000, 100000000000],
[100000000000, 100000000000, 100000000000, 100000000000, 100000000000],
[100000000000, 100000000000, 100000000000, 100000000000, 100000000000],
[100000000000, 100000000000, 100000000000, 100000000000, 100000000000],
[100000000000, 100000000000, 100000000000, 100000000000, 100000000000]])
kf = KalmanFilter((A, B, C, D, Q, R), init_mu, init_V)
kf.ys = obs
kf.us = conds
(y_pred, ll, _) = kf.smooth_filter()
(x, y, t) = y_pred.shape
pred = pd.DataFrame(y_pred.reshape((x, t)).T, columns=['Predicted Liquidity', 'Predicted Prices'])
# Projected prices
# (projection, V_projections) = kf.project(30)
# plt.plot(projection[1, :, :].flatten(), c='r')
# plt.show()
# Ordinary Least Squares Calculation
ols = sum((rmd.data.values[:, 2] - pred.values[:, 1]) * (rmd.data.values[:, 2] - pred.values[:, 1])) / 2
print(ols)
# Plot results
#area_ols if pred < low -> +, low <= pred <= high -> 0. high < pred -> +
ax = pred.plot(x=rmd.data[ccs.date.value], y=['Predicted Prices'])