--> Computer Science > Machine Learning arXiv:2604.06227 (cs) [Submitted on 27 Mar 2026] Title: A Benchmark of Classical and Deep Learning Models for Agricultural Commodity Price Forecasting on A Novel Bangladeshi Market Price Dataset Authors: Tashreef Muhammad , Tahsin Ahmed , Meherun Farzana , Md. Mahmudul Hasan , Abrar Eyasir , Md. Emon Khan , Mahafuzul Islam Shawon , Ferdous Mondol , Mahmudul Hasan , Muhammad Ibrahim View a PDF of the paper titled A Benchmark of Classical and Deep Learning Models for Agricultural Commodity Price Forecasting on A Novel Bangladeshi Market Price Dataset, by Tashreef Muhammad and 8 other authors View PDF HTML (experimental) Abstract: Accurate short-term forecasting of agricultural commodity prices is critical for food security planning and smallholder income stabilisation in developing economies, yet machine-learning-ready datasets for this purpose remain scarce in South Asia. This paper makes two contributions. First, we introduce AgriPriceBD, a benchmark dataset of 1,779 daily retail mid-prices for five Bangladeshi commodities - garlic, chickpea, green chilli, cucumber, and sweet pumpkin - spanning July 2020 to June 2025, extracted from government reports via an LLM-assisted digitisation pipeline. Second, we evaluate seven forecasting approaches spanning classical models - naïve persistence, SARIMA, and Prophet - and deep learning architectures - BiLSTM, Transformer, Time2Vec-enhanced Transformer, and Informer - with Diebold-Mariano statistical significance tests. Commodity price forecastability is fundamentally heterogeneous: naïve persistence dominates on near-random-walk commodities. Time2Vec temporal encoding provides no statistically significant advantage over fixed sinusoidal encoding and causes catastrophic degradation on green chilli (+146.1% MAE, p<0.001). Prophet fails systematically, attributable to discrete step-function price dynamics incompatible with its smooth decomposition assumptions. Informer produces erratic predictions (variance up to 50x ground-truth), confirming sparse-attention Transformers require substantially larger training sets than small agricultural datasets provide. All code, models, and data are released publicly to support replication and future forecasting research on agricultural commodity markets in Bangladesh and similar developing economies. Comments: 26 pages, 22 figures, 7 tables Subjects: Machine Learning (cs.LG) ; Econometrics (econ.EM) ACM classes: I.2.6; I.5.1; G.3; J.4 Cite as: arXiv:2604.06227 [cs.LG] (or arXiv:2604.06227v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2604.06227 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Tashreef Muhammad [ view email ] [v1] Fri, 27 Mar 2026 20:21:52 UTC (384 KB) Full-text links: Access Paper: View a PDF of the paper titled A Benchmark of Classical and Deep Learning Models for Agricultural Commodity Price Forecasting on A Novel Bangladeshi Market Price Dataset, by Tashreef Muhammad and 8 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.LG < prev | next > new | recent | 2026-04 Change to browse by: cs econ econ.EM References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation &times; loading... 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