## Time Series Analysis for Financial Data V — ARIMA Models

Download IPython Notebook here. In the previous posts in this series, we combined the Autoregressive models and Moving Average models to produce Auto Regressive Moving Average(ARMA) models. We found that we were still unable to fully explain autocorrelation or obtain residuals that are discrete white noise. Let’s further extend this discussion of Read more…

## Common Distributions and Random Variables

A random variable, X, is a variable quantity (i.e., not necessarily fixed) whose possible values depend on a set of random events. Like a traditional mathematical variable, its value is unknown a priori (before the outcome of the events is known) A random variable’s possible values might represent the possible outcomes Read more…

## Recapping Covariance and Correlation

Covariance Covariance measures the extent to which the relationship between two variables is linear. The sign of the covariance shows the trend in the linear relationship between the variables, i.e if they tend to move together or in separate directions. A positive sign indicates that the variables are directly related, Read more…

## Refresher: Expected Value and Arithmetic Mean

In this refresh article, we are going to take you back to basics and quickly recap expected value and different types of mean. Although this is basic it is important that you are fluent with these terms to master the more advanced lessons coming later in this series. The expected Read more…

## Toolbox Video Explainer

In this video, I give a quick explanation of the Auquan toolbox and how to use it to create your trading strategies. If you want to jump to certain topics, navigate to the times below or click the links: Importing data: 2:38 Importing and using premade features: 4:12 Inputting prediction Read more…

## Integration, Co-Integration and Stationarity Notebook Recap

Work through this notebook to refresh your understanding of Integration, co-integration and stationarity.