Smoothing data by using Kalman filter

1. What is Kalman Smoothing?
2. Why use Kalman smoother?
3. Can you explain how to use a Kalman filter for time series forecasting?
4. Why is Kalman filter better?

What is Kalman Smoothing?

The Kalman filter and smoother are a set of equations which efficiently compute the posterior distribution over the latent states of a linear state space model given some observed data. The Kalman equations do not carry out any learning.

Why use Kalman smoother?

Good reasons for Kalman smoothing are: The Kalman smoother provides very good imputations (i.e. imputed values) for missing values in your time series. The Kalman smoother provides very good estimates of the state vector in the historical period.

Can you explain how to use a Kalman filter for time series forecasting?

Kalman filter algorithm uses a series of measurements observed over time, containing noise and other inaccuracies, and produces estimates of unknown variables. This estimate tend to be more accurate than those based on a single measurement alone.

Why is Kalman filter better?

Kalman filters are used to optimally estimate the variables of interests when they can't be measured directly, but an indirect measurement is available. They are also used to find the best estimate of states by combining measurements from various sensors in the presence of noise.