> mapasimple(admissions)\r\n t+1 t+2 t+3 t+4 t+5 t+6 t+7 t+8 t+9 t+10 t+11 t+12 \r\n457438.0 446869.3 450146.7 462231.5 457512.8 467895.1 457606.0 441295.7 471611.2 454282.0 458308.0 453472.5<\/pre>\n
This also gives you a simple plot of the series and the forecast:
\n $\"\"$ <\/a>
\nI can ask for a detailed view of the predicted states at each temporal aggregation level:<\/p>\n
> mapasimple(admissions,outplot=2,paral=2)<\/pre>\n
$\"\"$ <\/a>
\nIn this example I also used paral=2<\/em>. This creates a parallel cluster, executes mapasimple<\/em> and then closes the cluster. If you already have a parallel cluster running you can use paral=1<\/em>.
\nUsing functions mapaest<\/em> and mapafor<\/em> I can have a more detailed control of the estimation and the forecasts across the different levels of temporal aggregation.<\/p>\n
> mapafit <- mapaest(admissions,paral=2)\r\n> mapafor(admissions,mapafit)<\/pre>\nThe first function estimates the model fit at each temporal aggregation level and also provides a visualisation of the identified ETS<\/em> components.
\n $\"mapaR.fig2\"$ <\/a>
\nThe second function provides in- and out-of-sample forecasts. By default only one step ahead in-sample forecasts are given.
\n $\"mapaR.fig3\"$ <\/a>
\nThis is easy to change, for instance to 12-steps ahead:<\/p>\n
> mapafor(admissions,mapafit,ifh=12,fh=0)<\/pre>\n
$\"mapaR.fig4\"$ <\/a>
\nI can stop plotting the output by setting outplot=0<\/em> in any of the above functions. The functions have are several more options, allowing you to set the maximum temporal aggregation level, the type of MAPA combination, etc. Finally, the function mapa<\/em> is a wrapper for both mapaest<\/em> and mapafor<\/em>, so these can be run with a single call.
\nSince version 1.5 of the package there are some new interesting features. The first one is to force a particular exponential smoothing model on all aggregation levels. This can be simply done by using the option model<\/em>:<\/p>\n
> mapa(admissions,model=\"AAdN\",paral=2)<\/pre>\nA nonseasonal damped trend model is fitted to the time series in this case. Since MAPA can no longer change between models and choose a simpler one, it is possible that the preselected model will have too many degrees of freedom for the aggregate version of a given series. In this case no model is fitted.
\n $\"mapaR.fig6\"$ <\/a>
\nFurthermore, if a seasonal model is selected, for any aggregation levels with non=integer seasonality a non-seasonal version of that model will be fitted.
\nAnother new option is the ability to calculate empirical prediction intervals. As these require simulating forecasts for their calculation, they are computationally expensive and not provided by default. To get the 80%, 90%, 95% and 99% prediction intervals you can use:<\/p>\n
> mapa(admissions,conf.lvl=c(0.8,0.9,0.95,0.99),paral=2)<\/pre>\n
$\"mapaR.fig7\"$ <\/a>
\nFor more details about the Multiple Aggregation Prediction Algorithm (MAPA) look at the this post<\/a>.<\/p>\n
Update<\/strong>: The MAPA package now allows modelling high-frequency<\/a> time series and include regressors (MAPAx<\/a>).<\/p>\n
Related Posts<\/H3>
Special issue on innovations in hierarchical forecasting<\/a><\/li>\n
Elucidate structure in intermittent demand time series<\/a><\/li>\n
Cross-temporal aggregation: Improving the forecast accuracy of hierarchical electricity consumption<\/a><\/li>\n<\/ul><\/div>","protected":false},"excerpt":{"rendered":"
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\u2026 Read More »<\/a><\/span><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[10],"tags":[45,38,39,36],"_links":{"self":[{"href":"https:\/\/kourentzes.com\/forecasting\/wp-json\/wp\/v2\/posts\/133"}],"collection":[{"href":"https:\/\/kourentzes.com\/forecasting\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/kourentzes.com\/forecasting\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/kourentzes.com\/forecasting\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/kourentzes.com\/forecasting\/wp-json\/wp\/v2\/comments?post=133"}],"version-history":[{"count":0,"href":"https:\/\/kourentzes.com\/forecasting\/wp-json\/wp\/v2\/posts\/133\/revisions"}],"wp:attachment":[{"href":"https:\/\/kourentzes.com\/forecasting\/wp-json\/wp\/v2\/media?parent=133"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/kourentzes.com\/forecasting\/wp-json\/wp\/v2\/categories?post=133"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/kourentzes.com\/forecasting\/wp-json\/wp\/v2\/tags?post=133"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}