linkalman 0.11.5

Flexible Linear Kalman Filter

linkalman

linkalman is a python package that solves linear structural time series models with Gaussian noises. Compared with some other popular Kalman filter packages written in python, linkalman has a combination of several advantages:

• Account for partially and fully incomplete measurements
• Flexible and convenient model structure
• Robust and efficient implementation
• Proper implementation for unknown priors
• Built-in numerical and EM algorithm
• Open-source with a comprehensive user manual
• Modular design with intuitive model specification

Installation

linkalman requires the following packages to run:

• numpy
• pandas
• networkx
• scipy

To install linkalman, simply use the standard pip command:

$ pip install linkalman

Example

Here I will provide a simple example using linkalman. See here for more examples, and user's manual for technical details.

import pandas as pd
import numpy as np
from scipy.optimize import minimize
from linkalman.models import BaseConstantModel as BCM
import matplotlib.pyplot as plt


# Get data
df = pd.read_csv('https://raw.githubusercontent.com/jbrownlee/Datasets/master/daily-total-female-births.csv')
df['x'] = 1
df.set_index('Date', inplace=True)

First we define the system dynamics of a Bayesian Structural Time Series (BSTS) model. Here I define a Stochastic linear trend model to extract the trend information from the time series (referring to the example section of user's manual for details)

def my_f(theta):
    sig1 = np.exp(theta[0])
    sig2 = np.exp(theta[1])
    sig3 = np.exp(theta[2])

    F = np.array([[1, 1], [0, 1]])
    Q = np.array([[sig1, 0], [0, sig2]]) 
    R = np.array([[sig3]])
    H = np.array([[1, 0]])
    # Collect system matrices
    M = {'F': F, 'Q': Q, 'H': H, 'R': R}

    return M 

Next we define a solver or optimizer, you can choose any solver you prefer. Here I just use scipy.optimize.minimize.

def my_solver(param, obj_func, verbose=False, **kwargs):
    obj_ = lambda x: -obj_func(x)
    res = minimize(obj_, param, **kwargs)
    theta_opt = np.array(res.x)
    fval_opt = res.fun
    return theta_opt, fval_opt

Now we can fit the data. First we initialize the model and feed the system dynamics (my_f) and solver (my_solver). You may also pass the keyworded arguments to for my_f and my_solver.

model = BCM()
model.set_f(my_f)
model.set_solver(my_solver, method='nelder-mead', 
        options={'xatol': 1e-8, 'disp': True, 'maxiter': 10000})
theta_init = np.random.rand(3)
model.fit(df, theta_init, y_col=['Births'], x_col=['x'], 
              method='LLY')
df_LLY = model.predict(df)

That is it! If you want to do additional work, you can do the following to plot a confidence interval around your predictions.

df_LLY['kf_ub'] = df_LLY.Births_filtered + 1.96 * np.sqrt(df_LLY.Births_fvar)
df_LLY['kf_lb'] = df_LLY.Births_filtered - 1.96 * np.sqrt(df_LLY.Births_fvar)
df_LLY = df_LLY[df_LLY.index > '1959-01-01']
df_LLY.index = pd.to_datetime(df_LLY.index)

# Define plot function
def simple_plot(df, col_est, col_actual, col_ub, col_lb, label_est,
                label_actual, title, figsize=(12, 8)):
    ax = plt.figure(figsize=figsize)
    plt.plot(df.index, df[col_est], 'r', label=label_est)
    plt.scatter(df_LLY.index, df[col_actual], s=20, c='b', 
                marker='o', label=label_actual)
    plt.fill_between(df.index, df[col_ub], df[col_lb], color='g', alpha=0.2)
    ax.legend(loc='right', fontsize=9)
    plt.title(title, fontsize=22)
    plt.show()
simple_plot(df_LLY, 'Births_filtered', 'Births', 'kf_ub', 'kf_lb',  
           'Prediction', 'Births', 'Filtered Births Data')

License

3-Clause BSD