{"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/well-googled-is-half-done-multimodal","title":"Well Googled is Half Done: Multimodal Forecasting of New Fashion Product Sales with Image-based Google Trends","arxiv_id":"2109.09824","date":"2021-09-20","proceeding":null,"authors":["Geri Skenderi","Christian Joppi","Matteo Denitto","Marco Cristani"],"abstract":"New fashion product sales forecasting is a challenging problem that involves many business dynamics and cannot be solved by classical forecasting approaches. In this paper, we investigate the effectiveness of systematically probing exogenous knowledge in the form of Google Trends time series and combining it with multi-modal information related to a brand-new fashion item, in order to effectively forecast its sales despite the lack of past data. In particular, we propose a neural network-based approach, where an encoder learns a representation of the exogenous time series, while the decoder forecasts the sales based on the Google Trends encoding and the available visual and metadata information. Our model works in a non-autoregressive manner, avoiding the compounding effect of large first-step errors. As a second contribution, we present VISUELLE, a publicly available dataset for the task of new fashion product sales forecasting, containing multimodal information for 5577 real, new products sold between 2016-2019 from Nunalie, an Italian fast-fashion company. The dataset is equipped with images of products, metadata, related sales, and associated Google Trends. We use VISUELLE to compare our approach against state-of-the-art alternatives and several baselines, showing that our neural network-based approach is the most accurate in terms of both percentage and absolute error. It is worth noting that the addition of exogenous knowledge boosts the forecasting accuracy by 1.5% in terms of Weighted Absolute Percentage Error (WAPE), revealing the importance of exploiting informative external information. The code and dataset are both available at https://github.com/HumaticsLAB/GTM-Transformer.","url_abs":"https://arxiv.org/abs/2109.09824v6","url_pdf":"https://arxiv.org/pdf/2109.09824v6.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":"well-googled-is-half-done-multimodal","repo_url":"https://github.com/humaticslab/gtm-transformer","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"new-product-sales-forecasting","task_name":"New Product Sales Forecasting"},{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"}],"methods":[{"method_slug":"absolute-position-encodings","method_name":"Absolute Position Encodings"},{"method_slug":"adam","method_name":"Adam"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"bpe","method_name":"BPE"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"label-smoothing","method_name":"Label Smoothing"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"position-wise-feed-forward-layer","method_name":"Position-Wise Feed-Forward Layer"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"transformer","method_name":"Transformer"}],"datasets_introduced":[{"slug":"visuelle","name":"VISUELLE","full_name":""}],"methods_introduced":[],"results":[{"leaderboard":"/sota/new-product-sales-forecasting-on-visuelle","task":"New Product Sales Forecasting","dataset":"VISUELLE","model":"GTM-Transformer [Extra Tag]","rank_in_archive_order":3,"of":5,"metrics":{"MAE":"29.6","WAPE":"54.2"},"uses_additional_data":false},{"leaderboard":"/sota/new-product-sales-forecasting-on-visuelle","task":"New Product Sales Forecasting","dataset":"VISUELLE","model":"GTM-Transformer","rank_in_archive_order":4,"of":5,"metrics":{"MAE":"30.2","WAPE":"55.2"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2109.09824","atlas_url":"https://app.syntology.ai/?focus=2109.09824","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2109.09824"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+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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