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Specifically, we exploit the expressiveness and temporal nature of\nSequence-to-Sequence Neural Networks (e.g. recurrent and convolutional\nstructures), the nonparametric nature of Quantile Regression and the efficiency\nof Direct Multi-Horizon Forecasting. A new training scheme,\n*forking-sequences*, is designed for sequential nets to boost stability and\nperformance. We show that the approach accommodates both temporal and static\ncovariates, learning across multiple related series, shifting seasonality,\nfuture planned event spikes and cold-starts in real life large-scale\nforecasting. The performance of the framework is demonstrated in an application\nto predict the future demand of items sold on Amazon.com, and in a public\nprobabilistic forecasting competition to predict electricity price and load.","url_abs":"http://arxiv.org/abs/1711.11053v2","url_pdf":"http://arxiv.org/pdf/1711.11053v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"a-multi-horizon-quantile-recurrent-forecaster","repo_url":"https://github.com/tianchen101/MQRNN","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"a-multi-horizon-quantile-recurrent-forecaster","repo_url":"https://github.com/ErezSC42/qr_forcaster","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"a-multi-horizon-quantile-recurrent-forecaster","repo_url":"https://github.com/awslabs/gluon-ts","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"mxnet","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"a-multi-horizon-quantile-recurrent-forecaster","repo_url":"https://github.com/jingw2/demand_forecast","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"a-multi-horizon-quantile-recurrent-forecaster","repo_url":"https://github.com/kshmawj111/solar_energy_forecast","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"mxnet","reach":{"status":"ok"}}],"tasks":[{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"},{"task_slug":"time-series-regression","task_name":"Time Series Regression"},{"task_slug":"quantile-regression","task_name":"quantile regression"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1711.11053","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1711.11053"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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