Exogenous variable driven deep learning models for improved price forecasting of TOP crops in India

G. H. Harish Nayak · Md Wasi Alam · K. N. Singh · G. Avinash · Rajeev Ranjan Kumar · Mrinmoy Ray +1 more

42 citations0/0 files storedScientific Reports

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Stock Market Forecasting Methods Smart Agriculture and AI Forecasting Techniques and Applications

Abstract

Accurately predicting agricultural commodity prices is crucial for India's economy. Traditional parametric models struggle with stringent assumptions, while machine learning (ML) approaches, though data-driven, lack automatic feature extraction. Deep learning (DL) models, with advanced feature extraction and predictive abilities, offer a promising solution. However, their application to agricultural price data ignored the exogenous factors. Hence, the study explored advanced versions of the well-known univariate models, NBEATSX and TransformerX. The research employed price data for essential crops like Tomato, Onion, and Potato (TOP) from major Indian markets and complemented it with corresponding weather data (precipitation and temperature). To provide a comprehensive analysis, the study also evaluated traditional statistical methods (ARIMAX and MLR) and a suite of ML algorithms (ANN, SVR, RFR, and XGBoost). The performance of these models was rigorously evaluated using error metrics like RMSE, MAE, sMAPE, MASE and QL. The findings were significant indicating DL models, particularly when augmented with exogenous variables, consistently outshone other methods with NBEATSX and TransformerX showing an average RMSE of 110.33 and 135.33, MAE of 60.08 and 74.92, sMAPE of 22.14 and 24.00, MASE of 1.02 and 1.32 and QL of 30.04 and 34.07, respectively. They exhibited lower error metrics, as compare to the statistical and ML models underscoring their effectiveness and potential in agricultural crop price forecasting. This study not only bridged a crucial research gap but also highlighted the robust potential of DL models in enhancing the accuracy of agricultural commodity price predictions in India.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 42 citations exist upstream.

References

2018Statistical and Machine Learning forecasting methods: Concerns and ways forward 2019Ensemble approach based on bagging, boosting and stacking for short-term prediction in agribusiness time series 2022Machine learning techniques for forecasting agricultural prices: A case of brinjal in Odisha, India 2023Agricultural Product Price Forecasting Methods: A Review 2021Deep long short-term memory based model for agricultural price forecasting 2021Forecasting spot prices of agricultural commodities in India: Application of deep‐learning models 2023Forecasting commodity prices: empirical evidence using deep learning tools 2024Hidden Markov guided Deep Learning models for forecasting highly volatile agricultural commodity prices

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.