arXiv：cs.LG· Mateusz Buczy\'nski, Micha{\l} Wo\'zniak, Konrad Kaczy\'nski, Anna Wr\'oblewska, Sebastian Kuk·· 3 小时前
二手电子产品多周期价格预测：深度学习与统计模型的系统性基准评测
Deep Learning vs. Statistical Models for Multi-Horizon Price Forecasting of Second-Hand Electronics: A Systematic Benchmark
AI 导读
一篇论文对二手电子产品价格预测进行了首个多周期系统性基准评测，使用波兰在线市场2022年1月至2025年3月、100多款手机和笔记本的每日价格数据，评估了11个模型在1至365天六个预测周期上的表现。
正文
Abstract:Forecasting resale prices of used electronics is critical for subscription-based platforms where pricing errors translate directly into risk. Unlike structured financial markets, second-hand electronics exhibit high volatility, sparse listing histories, and non-normal price dynamics - yet no systematic time-series benchmark exists for this domain. This paper presents the first multi-horizon benchmark of statistical and deep learning forecasting models for used electronics price prediction. We use a large-scale dataset of daily price listings from Polish online marketplaces (January 2022 to March 2025, 100+ smartphone and laptop models) and evaluate eleven models across six horizons from 1 to 365 days, covering classical methods (ARIMA, ETS, Theta), recurrent and convolutional networks (LSTM, TCN), and modern deep architectures (N-BEATS, N-HiTS, TFT, PatchTST, Informer). Three complementary evaluation protocols assess trajectory fitness, one-shot endpoint accuracy, and cross-horizon transfer. N-BEATS achieves the lowest MAPE beyond 30 days, reaching 8.51% at 365 days versus 14.94% for the best statistical baseline - a 43% reduction. At short horizons (1-7 days), all models converge near 0.72% MAPE and the naive baseline remains competitive. A single N-BEATS model trained at 365 days generalizes to all shorter horizons, eliminating the need for horizon-specific models. N-BEATS and N-HiTS also demonstrate superior hyperparameter stability.
| Comments: | 41 pages, 7 figures, 13 tables |
| Subjects: | Computational Finance (q-fin.CP); Machine Learning (cs.LG) |
| MSC classes: | 62M10, 68T07 |
| ACM classes: | I.2.6; I.5.4 |
| Cite as: | arXiv:2610.10727 [q-fin.CP] |
| (or arXiv:2610.10727v1 [q-fin.CP] for this version) | |
| https://doi.org/10.48550/arXiv.2610.10727 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Mateusz Buczyński [view email]
[v1]
Wed, 7 Oct 2026 18:05:13 UTC (951 KB)
来源：arXiv：cs.LG · arxiv.org