{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/ensemble-sales-forecasting-study-in","title":"Ensemble Sales Forecasting Study in Semiconductor Industry","arxiv_id":"1705.00003","date":"2017-04-28","proceeding":null,"authors":["Qiuping Xu","Vikas Sharma"],"abstract":"Sales forecasting plays a prominent role in business planning and business\nstrategy. The value and importance of advance information is a cornerstone of\nplanning activity, and a well-set forecast goal can guide sale-force more\nefficiently. In this paper CPU sales forecasting of Intel Corporation, a\nmultinational semiconductor industry, was considered. Past sale, future\nbooking, exchange rates, Gross domestic product (GDP) forecasting, seasonality\nand other indicators were innovatively incorporated into the quantitative\nmodeling. Benefit from the recent advances in computation power and software\ndevelopment, millions of models built upon multiple regressions, time series\nanalysis, random forest and boosting tree were executed in parallel. The models\nwith smaller validation errors were selected to form the ensemble model. To\nbetter capture the distinct characteristics, forecasting models were\nimplemented at lead time and lines of business level. The moving windows\nvalidation process automatically selected the models which closely represent\ncurrent market condition. The weekly cadence forecasting schema allowed the\nmodel to response effectively to market fluctuation. Generic variable\nimportance analysis was also developed to increase the model interpretability.\nRather than assuming fixed distribution, this non-parametric permutation\nvariable importance analysis provided a general framework across methods to\nevaluate the variable importance. This variable importance framework can\nfurther extend to classification problem by modifying the mean absolute\npercentage error(MAPE) into misclassify error. Please find the demo code at :\nhttps://github.com/qx0731/ensemble_forecast_methods","url_abs":"http://arxiv.org/abs/1705.00003v3","url_pdf":"http://arxiv.org/pdf/1705.00003v3.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":"ensemble-sales-forecasting-study-in","repo_url":"https://github.com/qx0731/ensemble_forecast_methods","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":null,"task_name":"CPU"},{"task_slug":"time-series","task_name":"Time Series Analysis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}