nnfor on github

I have put up a github repository for the nnfor package for R: https://github.com/trnnick/nnfor I will be putting updates and fixes there, before they are pushed on CRAN. You can also report there bugs. You can install the current github version with: Related PostsDiscussion panel on ‘AI in research’ at Skövde OR62 -The quest for… Read More »

Intermittent demand forecasting package for R

A new package for analysing and forecasting intermittent demand time series and slow moving items has been release for R. You can download the latest version from CRAN. The launch version contains the following functions: crost: Croston’s method and variants. crost.ma: Moving average with Croston’s method decomposition. idclass: Time series categorisation for intermittent demand. simID:… Read More »

Critical values for the Nemenyi test

The critical distance for the Nemenyi test is calculated as: , where is the confidence level, is the number of models and is the number of measurements. To calculate the Studentised range statistic for infinite degrees of freedom divided by is used. The values of for up to can be download here as csv. You… Read More »

TStools for R

This is a collection of functions for time series analysis/modelling for R. Follow link to GitHub. If you need help installing this package in R have a look at this post. Alternatively just type in R the following commands: > if (!require(“devtools”)) install.packages(“devtools”) > devtools::install_github(“trnnick/TStools”) At the time of posting the following functions are included:… Read More »

Multiple Aggregation Prediction Algorithm (MAPA)

MAPA code for R is available on CRAN. An online interactive demo of MAPA can be found here. Here is a quick demonstration what you can do with the code. The easiest way to produce a forecast with MAPA is to use the mapasimple function. > mapasimple(admissions) t+1 t+2 t+3 t+4 t+5 t+6 t+7 t+8… Read More »

ANOM and Nemenyi tests

Code for the ANOM and Nemenyi tests for MatLab. Download here. For a discussion of the two tests and the various ways to visualise the results look at this post. Here are some examples, using the M3 results: >> anom(X,0.05,labels); The models in red are significantly better than the average (solid line). For the Nemenyi… Read More »