Download Report IEEE DataPort : Dataset for Hybrid Deep Learning Model for Wheat Price Forecasting - 2025
CSV by Yelda FIRAT, Hüseyin Ali SARIKAYA
Information
Format: CSV Publisher: IEEE DataPort Publication Date of the Electronic Edition: 12/21/2025
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ISBN: 10.21227/jtct-4d19
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Description
This dataset comprises 38 ,019 daily transaction records for various wheat products collected from a Turkish commodities exchange between 1 June 2022 and 4 May 2023. Each row contains the transaction date, product class and name, estimated quantity, and unit price, along with 18 quality‑related attributes such as moisture content, hectolitre weight, protein percentage, insect damage, foreign material, weed contamination, broken or shrivelled grains, and other defect metrics. The resulting multivariate time series enables research on wheat price forecasting, quality assessment, and anomaly detection. The data are provided in CSV format and were used in our study “A Hybrid Deep Learning Model for Wheat Price Forecasting: LSTM–Autoencoder Ensemble Approach” to train and evaluate ensemble models combining long short‑term memory (LSTM) networks and autoencoders.
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