Message-ID: <CAP+bYWC9hDD11V3kZ2_8aEG+avEsekDOC3M+jPTzZEiFLnw89w@mail.gmail.com>
Date: 2013-07-02T03:22:05Z
From: Hasan Diwan
Subject: KalmanForecast (stats)
In-Reply-To: <A1F1A2DDE4BBE14F8DD8B25666C9D5A024B5C988@ex-mbx2.uark.edu>
On 1 July 2013 19:24, Giovanni Petris <gpetris at uark.edu> wrote:
> Could you send me a simple example of KalmanForecast (with input data) that I can run and can see how it works exactly?
There's an explanation of the Kalman Filter available at
http://www.swarthmore.edu/NatSci/echeeve1/Ref/Kalman/ScalarKalman.html
-- I've summarised it below:
The kalman filter is used to reduce the noise in an indirectly
measured signal, s, approximated by the formula -- x[t] = a*x[t-1] +
b*u[t], to which a random amount of white noise is added, making the
equation x[t] = a*x[t-1]+b*u[t] + w[t]. The white noise varies with
time, hence it's a series. Each measure of x[t] brings you closer to
the actual signal. I hope this helps... -- H
--
Sent from my mobile device
Envoy? de mon portable