{"id":1363,"date":"2017-06-29T17:18:53","date_gmt":"2017-06-29T17:18:53","guid":{"rendered":"http:\/\/kourentzes.com\/forecasting\/?p=1363"},"modified":"2017-06-30T16:28:17","modified_gmt":"2017-06-30T16:28:17","slug":"international-journal-of-forecasting-2014-2015-best-paper-award","status":"publish","type":"post","link":"https:\/\/kourentzes.com\/forecasting\/2017\/06\/29\/international-journal-of-forecasting-2014-2015-best-paper-award\/","title":{"rendered":"International Journal of Forecasting 2014-2015 best paper award"},"content":{"rendered":"

In the very enjoyable and stimulating International Symposium on Forecasting<\/a> that just finished in Cairns, Australia, the International Journal of Forecasting (IJF<\/a>) best paper award for the years 2014-2015 (list of past papers can be found here<\/a>) was given to one of my papers<\/a>: Improving forecasting by estimating time series structural components across multiple frequencies!<\/p>\n

This paper proposes the use of Multiple Temporal Aggregation<\/a> approach that I have been posting about, and introduces the MAPA forecasting method, for which there is an R package<\/a>. The other shortlisted papers were of very good quality and I am humbled by the choice of the editorial board. My thanks to my co-authors and the reviewers, who made this paper possible.<\/p>\n

$\"\"$ <\/a> $\"\"$ <\/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":"
In the very enjoyable and stimulating International Symposium on Forecasting that just finished in Cairns, Australia, the International Journal of Forecasting (IJF) best paper award for the years 2014-2015 (list of past papers can be found here) was given to one of my papers: Improving forecasting by estimating time series structural components across multiple frequencies!\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":[41],"tags":[62,38,36],"_links":{"self":[{"href":"https:\/\/kourentzes.com\/forecasting\/wp-json\/wp\/v2\/posts\/1363"}],"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=1363"}],"version-history":[{"count":2,"href":"https:\/\/kourentzes.com\/forecasting\/wp-json\/wp\/v2\/posts\/1363\/revisions"}],"predecessor-version":[{"id":1367,"href":"https:\/\/kourentzes.com\/forecasting\/wp-json\/wp\/v2\/posts\/1363\/revisions\/1367"}],"wp:attachment":[{"href":"https:\/\/kourentzes.com\/forecasting\/wp-json\/wp\/v2\/media?parent=1363"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/kourentzes.com\/forecasting\/wp-json\/wp\/v2\/categories?post=1363"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/kourentzes.com\/forecasting\/wp-json\/wp\/v2\/tags?post=1363"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}