---
title: Newsvendor Problem: High-Dimensional Data and Mixed-Frequency Method
url: https://www.ml-quant.com/papers/repec/eee-proeco-v-266-y-2023-i-c-s0925527323002748/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: RePEc:eee:proeco:v:266:y:2023:i:c:s0925527323002748
source_url: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS0925527323002748%3Bh%3Drepec%3Aeee%3Aproeco%3Av%3A266%3Ay%3A2023%3Ai%3Ac%3As0925527323002748
featured: 2023-11-08
citations: unknown
topic: ML & AI Methods
---


# Newsvendor Problem: High-Dimensional Data and Mixed-Frequency Method

High-Dimensional Data and Mixed-Frequency Method: The first article explores the application of machine learning to improve demand prediction and restocking decisions in newsvendor problems, utilizing complex and varied historical data.

- Source: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS0925527323002748%3Bh%3Drepec%3Aeee%3Aproeco%3Av%3A266%3Ay%3A2023%3Ai%3Ac%3As0925527323002748
- Identifier: RePEc:eee:proeco:v:266:y:2023:i:c:s0925527323002748
- Released: 2023-11-08
- First featured: Quant Letter No. 25 (2023-11-08): https://www.ml-quant.com/issues/2023-11-08/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: ML & AI Methods

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