Past experiments have demonstrated that humans (with or without formal training) are quite good at visually identifying the structure of time series. Trend is a key component, and arguably the most relevant to practice, as many of the forecasts that affect our lives have to do with potential increases or decreases of economic variables. Forecasters… Read More: Can you spot trend in time series? »
We have been re-working the TStools package over the past couple of weeks. The major changes are: The es function that is an alternative to the ets function from the forecast package has been removed. Now it is published separately in the smooth package, which contains a collection of interesting implementations for exponential smoothing, ARIMA… Read More: TStools recent changes »
Seasonality is a common characteristic of time series. It can appear in two forms: additive and multiplicative. In the former case the amplitude of the seasonal variation is independent of the level, whereas in the latter it is connected. The following figure highlights this: Note that in the example of multiplicative seasonality the season is… Read More: Additive and multiplicative seasonality – can you identify them correctly? »
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 »
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 »
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: ANOM and Nemenyi tests »
M. Hibon, S. F. Crone and N. Kourentzes, 2012, The 32nd Annual international Symposium on Forecasting, Boston.