標(biāo)題: Titlebook: Demand Prediction in Retail; A Practical Guide to Maxime C. Cohen,Paul-Emile Gras,Renyu Zhang Textbook 2022 The Editor(s) (if applicable) a [打印本頁] 作者: 味覺沒有 時間: 2025-3-21 17:04
書目名稱Demand Prediction in Retail影響因子(影響力)
書目名稱Demand Prediction in Retail影響因子(影響力)學(xué)科排名
書目名稱Demand Prediction in Retail網(wǎng)絡(luò)公開度
書目名稱Demand Prediction in Retail網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Demand Prediction in Retail被引頻次
書目名稱Demand Prediction in Retail被引頻次學(xué)科排名
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書目名稱Demand Prediction in Retail年度引用學(xué)科排名
書目名稱Demand Prediction in Retail讀者反饋
書目名稱Demand Prediction in Retail讀者反饋學(xué)科排名
作者: 聲明 時間: 2025-3-22 00:14 作者: 津貼 時間: 2025-3-22 03:19
Common Demand Prediction Methods,n explain how to properly structure the dataset. We next discuss two approaches in terms of data aggregation: the centralized approach (which combines the data across all SKUs and estimate a single model) and the decentralized approach (which estimates a different model for each SKU by solely relyin作者: 暖昧關(guān)系 時間: 2025-3-22 07:34
Tree-Based Methods,ypes of methods: Decision Tree, Random Forest, and Gradient Boosted Tree. We apply these methods under both the centralized and decentralized approaches. For each method, we briefly discuss the underlying mathematical framework, present a common practical way to select the parameters, and detail the作者: sulcus 時間: 2025-3-22 08:50
Clustering Techniques, aggregate the data across different SKUs to improve the prediction accuracy. On the one hand, aggregating sales data across several SKUs will help reduce the noise and would allow the model to rely on a larger number of observations. On the other hand, this will overlook the fact that each SKU bear作者: Dedication 時間: 2025-3-22 14:05
More Advanced Methods,is an open-sourced library released by Facebook researchers in 2017. Prophet is a time-series demand prediction method that often performs well on large-scale problems. We explain the method and discuss its implementation both with and without incorporating features. We then present a method that ca作者: Dedication 時間: 2025-3-22 18:43
Conclusion and Advanced Topics,cs related to demand prediction, such as deep learning methods, transfer learning, and data censoring. For each topic, we provide a number of relevant references for interested readers. We close by discussing several decisions that can be guided by prescriptive analytics tools that rely on demand pr作者: reflection 時間: 2025-3-23 00:32 作者: 深陷 時間: 2025-3-23 01:54 作者: 夾死提手勢 時間: 2025-3-23 06:26 作者: Axon895 時間: 2025-3-23 09:45
Evaluation and Visualization,In this chapter, we summarize all the results obtained so far and compare the different methods in terms of prediction accuracy. We then present several simple ways to visualize and communicate the prediction results. Finally, we consider varying the ratio of the train-test data split to assess the robustness of our results.作者: FACET 時間: 2025-3-23 16:47 作者: expansive 時間: 2025-3-23 21:43
Demand Prediction in Retail978-3-030-85855-1Series ISSN 2365-6395 Series E-ISSN 2365-6409 作者: 嚴(yán)厲批評 時間: 2025-3-23 23:55 作者: Vertebra 時間: 2025-3-24 03:10 作者: 男生如果明白 時間: 2025-3-24 07:01 作者: obeisance 時間: 2025-3-24 13:01 作者: accomplishment 時間: 2025-3-24 15:25
https://doi.org/10.1007/978-3-531-92479-3ypes of methods: Decision Tree, Random Forest, and Gradient Boosted Tree. We apply these methods under both the centralized and decentralized approaches. For each method, we briefly discuss the underlying mathematical framework, present a common practical way to select the parameters, and detail the作者: 啪心兒跳動 時間: 2025-3-24 19:02
Die Privatisierung von Krankenh?usern aggregate the data across different SKUs to improve the prediction accuracy. On the one hand, aggregating sales data across several SKUs will help reduce the noise and would allow the model to rely on a larger number of observations. On the other hand, this will overlook the fact that each SKU bear作者: 擦試不掉 時間: 2025-3-25 00:25 作者: 創(chuàng)作 時間: 2025-3-25 07:06
Ines Gottschalk,Dilek Aysel Tepelics related to demand prediction, such as deep learning methods, transfer learning, and data censoring. For each topic, we provide a number of relevant references for interested readers. We close by discussing several decisions that can be guided by prescriptive analytics tools that rely on demand pr作者: 闡明 時間: 2025-3-25 08:47
Maxime C. Cohen,Paul-Emile Gras,Renyu ZhangCovers the entire process of demand prediction for any business setting.Discusses all the steps required in a real-world implementation.Includes additional material to assist the learning experience作者: 柱廊 時間: 2025-3-25 12:45 作者: 惹人反感 時間: 2025-3-25 17:34 作者: Facet-Joints 時間: 2025-3-25 21:57
Textbook 2022 It enables readers to understand how to leverage data to predict future demand, how to clean and pre-process the data to make it suitable for predictive analytics, what the common caveats are in terms of implementation and how to assess prediction accuracy..作者: 火海 時間: 2025-3-26 01:51
https://doi.org/10.1007/978-3-662-42497-1discuss the objective and scope considered in this book by elaborating on the concepts of training and test data, presenting several demand prediction accuracy metrics, and pinpointing the specific application under consideration.作者: 運動吧 時間: 2025-3-26 07:36 作者: Graphite 時間: 2025-3-26 10:14 作者: 粘土 時間: 2025-3-26 13:58
Introduction,discuss the objective and scope considered in this book by elaborating on the concepts of training and test data, presenting several demand prediction accuracy metrics, and pinpointing the specific application under consideration.作者: plasma 時間: 2025-3-26 18:20 作者: 長矛 時間: 2025-3-27 00:48
Clustering Techniques,d prediction model for each SKU by relying on the historical data from all the SKUs in the same cluster. We consider two common clustering techniques: k-means and DBSCAN and implement them using the accompanying dataset.作者: 減弱不好 時間: 2025-3-27 02:07
Textbook 2022demand for retailers. Each step is illustrated with the relevant code and implementation details to demystify how historical data can be leveraged to predict future demand. The tools and methods presented can be applied to most retail settings, both online and brick-and-mortar, such as fashion, elec作者: Jocose 時間: 2025-3-27 06:20 作者: entitle 時間: 2025-3-27 12:49
https://doi.org/10.1007/978-3-531-92479-3es. For each method, we briefly discuss the underlying mathematical framework, present a common practical way to select the parameters, and detail the implementation process by providing the appropriate codes. We conclude by comparing the different methods in terms of both prediction accuracy and running time.作者: 四溢 時間: 2025-3-27 15:58 作者: 反饋 時間: 2025-3-27 20:47 作者: Filibuster 時間: 2025-3-27 23:43
Tree-Based Methods,es. For each method, we briefly discuss the underlying mathematical framework, present a common practical way to select the parameters, and detail the implementation process by providing the appropriate codes. We conclude by comparing the different methods in terms of both prediction accuracy and running time.作者: Obverse 時間: 2025-3-28 05:56
,Einführung in die Problemstellung, as accounting for time effects and constructing lag-price variables. We end this chapter by discussing the practice of scaling features, and how to sort and export the resulting processed dataset. Each step is illustrated using the accompanying dataset.作者: 可忽略 時間: 2025-3-28 06:34
Die Problematisierung sozialer Gruppenn strike a good balance between data aggregation (i.e., finding the right data granularity level) and demand prediction accuracy. We present the method, discuss how to fine-tune its hyperparameters, and conclude by interpreting the results obtained on the accompanying dataset.作者: 平靜生活 時間: 2025-3-28 13:35
Marcus Sch?gel,Inga Schmidt,Achim Sauerch — what he refers to as his ‘chosen road’. He writes:.In reflecting about his method he then continues:.I will take my first steps from these reflections to now begin to tell the story of a highly original and intense intellectual adventure.作者: Geyser 時間: 2025-3-28 16:48 作者: Petechiae 時間: 2025-3-28 21:42
