Visualizing Random Walk Data, Part 1

From the series: Visualizing Random Walk Data

Doug Hull, MathWorks

(Originally posted on Doug's MATLAB Video Tutorials blog.)

In this video we start with some data from a random process. Each piece of data represents a small time period and change in value. Think of it as something like a stock ticker, very noisy with a larger trend upward. From the patterns in this random data we can visualize it and try to predict what that phenomenon might do in the future. We will get a range of realistic futures and a sense of what the average looks like.

Polynomial fitting is problematic, so instead bootstraping of data will be tried. This week show the results, and the next two weeks show how it was done.

Product Focus

Recorded: 26 Dec 2012

Series: Visualizing Random Walk Data

3:39

Part 1 In this video we start with some data from a random process. Each piece of data represents a small time period and change in value. Think of it as something like a stock ticker, very noisy with a larger trend upward. From the patterns in this random

4:17

Part 2 This week we do a code review of some random walk data, making a heatmap with adjoining histograms to better understand the distribution of data. It shows the implementation of visualizations discussed earlier.

3:37

Part 3 Continuing with the random walk data: We will be bootstrap sampling the prior data to get estimates of what the next series of samples could look like.