I uploaded a new version (1.7) of tsintermittent on CRAN. Apart from fixing a couple of minor issues, a new function has been added to help scaling up forecasting. Recently I had a few requests to add a functionality to use data frames with multiple time series as inputs. I have included a new wrapper… Read More: Forecasting multiple time series with tsintermittent »
From time to time people have asked me how to implement Holt Winters (trend-seasonal exponential smoothing) in Excel. I have my reservations for using Excel to do your day-to-day forecasting. Nonetheless, you can find an example here.
I uploaded today to CRAN an updated version of tsintermittent. Yesterday I found a bug in the optimisation of the Croston’s, SBA and TSB methods and this update fixes that. Here is the changelog for this update: Version 1.3 (04 August 2014) * Corrected major optimisation bug – issue with opt.on in all crost, tsb… Read More: Update for the tsintermittent package »
Shiny is a web application framework for R. The idea is simple: deploy R code in webpages. This might prove useful when user interaction is required, for instance to design and deploy forecasting experiments that need human participants. I gave it a try to see how easy is it to build a demo. Assuming your… Read More: Experimenting with Shiny for R »
Recently I posted about a paper I co-authored with Fotios Petropoulos, now in JORS: Forecast Combinations for Intermittent Demand. There we found that for intermittent demand data using multiple levels of temporal aggregation, forecasting them with the appropriate models and finally combining the forecasts performed best. This approach has many analogies with the MAPA algorithm… Read More: MAPA and intermittent demand forecasting »
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: Intermittent demand forecasting package for R »
Amongst various minor improvements a few interesting new functions have been added to TStools package for R: Theta method ABC-XYZ analysis Theta method was found to be the most accurate in the M3 forecasting competition, but since then there has been limited use of the method. As it was later shown, the M3 competition Theta… Read More: Update for TStools »
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: Critical values for the Nemenyi test »
Update: The following R commands will install a package directly from GitHub to R: > if (!require(“devtools”)) install.packages(“devtools”) > devtools::install_github(“YYY/ZZZ”) where YYY is the GitHub username and ZZZ is the package name. For example to install TStools use: > devtools::install_github(“trnnick/TStools”) This makes the process described below unnecessary, however if you want the ‘full’ story read… Read More: How to use R packages from GitHub »
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: TStools for R »