Time Series Analysis 1 – Identifying Structure

In this article we tackle a generated set of progressively more complex time series datasets. From a random series to an ARIMA series with seasonality as well as a series with a structural change. For each of these time series we apply the traditional techniques used in time series analysis to ascertain the underlying structure. In a follow up article we will make the final step to use what we've learned to forecast into the future.

Naive Bayes

Let’s focus on this table. Subscript, , is used to represent the feature/dimension. Superscript, , is used to represent the observation (here the observation). First, we make some basic assertions about the data. Distributions Each has a Categorical distribution: (1) The following is equivalent: (2) , and is the probability…

Hand Image Classification – Part 2 – Transfer Learning – Which Hand?

The journey we took from simple algorithms to more complex ones resulted in a Convolutional Neural Network (CNN) with the ability to perfectly classify the images of the hands it had not seen yet. In this article we would like to build an algorithm to classify whether the hand in an image is a left or right hand. We can then re-use our well performing model in our new task. This is called Transfer Learning.

Gambler’s Ruin

Suppose I start gambling with $13 and with the goal of walking away with double my money. If I have 55% probability of winning $1 and 45% probability of losing $1 at each gamble, what's the probability that I will lose all my money before I double it? This is the Gambler's Ruin problem.

This article has 4 sections:

  1. Introduction: Introduction to the gambler's ruin problem
  2. Theory: The solution to the Gambler's Ruin problem
  3. Methodology: How the simulation will be carried out
  4. Pseudocode: Summary of the Python Code