simdkalman

import numpy as np import simdkalman # Generate some synthetic data for 3 series, 50 time steps each n_series = 3 n_timesteps = 50 rand = np.random.RandomState(0) true_states = np.cumsum(rand.normal(0, 0.1, size=(n_series, n_timesteps)), axis=1) observations = true_states + rand.normal(0, 1, size=(n_series, n_timesteps)) # Initialize a Kalman Filter # Simple 1-D random walk model: # state_transition = [[1]] (state at t is state at t-1) # process_noise = [[0.1]] (noise in state transition) # observation_model = [[1]] (observation is directly the state) # observation_noise = [[1.0]] (noise in observation) kf = simdkalman.KalmanFilter( state_transition = np.array([[1]]), # 1D state, previous state is current state process_noise = np.diag([0.1]), # Variance of 0.1 for state evolution observation_model = np.array([[1]]), # Observation is the state itself observation_noise = np.diag([1.0]) # Observation noise variance of 1.0 ) # Smooth the observations # `observations` needs to be 3D: (n_series, n_timesteps, n_features) # Here n_features is 1 for 1D data. smoothed_states = kf.smooth(observations[:, :, np.newaxis]).states.mean print(f"Smoothed states shape: {smoothed_states.shape}") # Expected: (n_series, n_timesteps, 1) # You can also predict new values predicted_states = kf.predict(observations[:, :, np.newaxis], n_test=10).states.mean print(f"Predicted states shape: {predicted_states.shape}")