The Theory Behind ARIMA

Bidyut Ghosh explains how the ARIMA forecasting method works:

The earlier models of time series are based on the assumptions that the time series variable is stationary (at least in the weak sense).

But in practical, most of the time series variables will be non-stationary in nature and they are intergrated series.

This implies that you need to take either the first or second difference of the non-stationary time series to convert them into stationary.

Bidyut ends with a little bit of implementation in R, but I’d guess that’ll be the focus of part 2.

Related Posts

Issues Starting ML Services

Jen Stirrup has a quick rundown of some reasons why Machine Learning Services might give you an error when you try to start it up: Msg 39023, Level 16, State 1, Procedure sp_execute_external_script, Line 1 [Batch Start Line 3] ‘sp_execute_external_script’ is disabled on this instance of SQL Server. Use sp_configure ‘external scripts enabled’ to enable […]

Read More

Using Have I Been Pwned In R

Maelle Salmon shows us how to use the HIBPwned library in R: The alternative title of this blog post is HIBPwned version 0.1.7 has been released! W00t!. Steph’s HIBPwned package utilises the HaveIBeenPwned.com API to check whether email addresses and/or user names have been present in any publicly disclosed data breach. In other words, this package potentially delivers bad news, but useful […]

Read More

Leave a Reply

Your email address will not be published. Required fields are marked *

Categories

April 2018
MTWTFSS
« Mar  
 1
2345678
9101112131415
16171819202122
23242526272829
30