And the win­ners are … Jeremy Howard and Lee C Baker. (See my ear­lier post for infor­ma­tion about the competition.)

Jeremy describes his approach to sea­sonal time series in a blog post on Kag​gle​.com. Lee described his approach to annual time series in an ear­lier post.

A few lessons that come out of this:

• For data from a sin­gle indus­try, using a global trend (i.e., esti­mated across all series) can be useful.
• Com­bin­ing fore­casts is a good idea. (This les­son seems to be re-​​learned in every fore­cast­ing competition!)
• The MASE can be very sen­si­tive to a few series, and to opti­mize MASE it is worth con­cen­trat­ing on these. (This is actu­ally not a good mes­sage for fore­cast­ing over­all, as we want good fore­casts for all series. Maybe we need to find a met­ric with sim­i­lar prop­er­ties to MASE but with a less skewed distribution.)
• Out­lier removal before fore­cast­ing can be effec­tive. (This is an inter­est­ing result as out­lier removal algo­rithms used in the M3 com­pe­ti­tion did not help fore­cast accuracy.)

Jeremy and Lee receive $500 for their efforts and they have decided to donate their prize money to the Fred Hol­lows Foun­da­tion. $500 will restore vision to 20 peo­ple. They will also write up their meth­ods in more detail for the Inter­na­tional Jour­nal of Fore­cast­ing. I am hope­ful that Philip Brier­ley of team Sali Mali (who did very well in the sec­ond stage of the com­pe­ti­tion) will also write a short expla­na­tion of his meth­ods for the IJF.

Thanks to every­one who par­tic­i­pated in the com­pe­ti­tion. Thanks also to Anthony Gold­bloom from Kag­gle for host­ing the com­pe­ti­tion. Kag­gle is a won­der­ful plat­form for pre­dic­tion com­pe­ti­tions and I hope it will be used for many more com­pe­ti­tions of this type in the future.

Related Posts:

• Tourism fore­cast­ing com­pe­ti­tion results: part one
• The tourism fore­cast­ing competition
• Global Energy Fore­cast­ing Competition
• Crowd sourc­ing forecasts
• Kag­gle on TV