Generating quantile forecasts in R

From today’s email:

I have just fin­ished read­ing a copy of ‘Forecasting:Principles and Prac­tice’ and I have found the book really inter­est­ing. I have par­tic­u­larly enjoyed the case stud­ies and focus on prac­ti­cal applications.

After fin­ish­ing the book I have joined a fore­cast­ing com­pe­ti­tion to put what I’ve learnt to the test. I do have a cou­ple of queries about the fore­cast­ing out­puts required. The out­put required is a quan­tile fore­cast, is this the same as pre­dic­tion inter­vals? Is there any R func­tion to pro­duce quan­tiles from 0 to 99?

If you were able to point me in the right direc­tion regard­ing the above it would be greatly appreciated.

Many Thanks,

Con­tinue reading →

Varian on big data

Last week my research group dis­cussed Hal Varian’s inter­est­ing new paper on “Big data: new tricks for econo­met­rics”, Jour­nal of Eco­nomic Per­spec­tives, 28(2): 3–28.

It’s a nice intro­duc­tion to trees, bag­ging and forests, plus a very brief entrée to the LASSO and the elas­tic net, and to slab and spike regres­sion. Not enough to be able to use them, but ok if you’ve no idea what they are. Con­tinue reading →

Specifying complicated groups of time series in hts

With the lat­est ver­sion of the hts pack­age for R, it is now pos­si­ble to spec­ify rather com­pli­cated group­ing struc­tures rel­a­tively easily.

All aggre­ga­tion struc­tures can be rep­re­sented as hier­ar­chies or as cross-​​products of hier­ar­chies. For exam­ple, a hier­ar­chi­cal time series may be based on geog­ra­phy: coun­try, state, region, store. Often there is also a sep­a­rate prod­uct hier­ar­chy: prod­uct groups, prod­uct types, packet size. Fore­casts of all the dif­fer­ent types of aggre­ga­tion are required; e.g., prod­uct type A within region X. The aggre­ga­tion struc­ture is a cross-​​product of the two hierarchies.

This frame­work includes even appar­ently non-​​hierarchical data: con­sider the sim­ple case of a time series of deaths split by sex and state. We can con­sider sex and state as two very sim­ple hier­ar­chies with only one level each. Then we wish to fore­cast the aggre­gates of all com­bi­na­tions of the two hierarchies.

Any num­ber of sep­a­rate hier­ar­chies can be com­bined in this way. Non-​​hierarchical fac­tors such as sex can be treated as single-​​level hier­ar­chies. Con­tinue reading →

New jobs in business analytics at Monash

We have an excit­ing new ini­tia­tive at Monash Uni­ver­sity with some new posi­tions in busi­ness ana­lyt­ics. This is part of a plan to strengthen our research and teach­ing in the data science/​computational sta­tis­tics area. We are hop­ing to make mul­ti­ple appoint­ments, at junior and senior lev­els. These are five-​​year appoint­ments, but we hope that the posi­tions will con­tinue after that if we can secure suit­able fund­ing. Con­tinue reading →

Generating tables in LaTeX

Typ­ing tables in LaTeX can get messy, but there are some good tools to sim­plify the process. One I dis­cov­ered this week is tables​gen​er​a​tor​.com, a web-​​based tool for gen­er­at­ing LaTeX tables. It also allows the table to saved in other for­mats includ­ing HTML and Mark­down. The inter­face is sim­ple, but it does most things. For com­pli­cated tables, some addi­tional for­mat­ting may be nec­es­sary. Con­tinue reading →