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STL-LSTM Hybrid Model for Forecasting Seasonal Agricultural Price Series

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Abstract

In this study, we propose an ensemble hybrid model called seasonal trend decomposition based on Loess and long short-term memory (STL-LSTM) for forecasting non-stationary, nonlinear, and seasonal agricultural price series. The model integrates STL, a decomposition technique, with a recurrent neural network-based forecasting method, long short-term memory (LSTM). The STL technique decomposes the original price series into its seasonal, trend, and remainder components, while the LSTM network forecasts each component individually. The predictions from all components are then aggregated to produce the final ensemble forecast. The developed model effectively captures the temporal patterns of complex time series through the analysis of the simplified decomposed components. Empirical results from monthly potato price data of two major markets in India reveal the superior forecasting ability of the proposed model. The STL-LSTM achieves up to 12% lower root mean square error (RMSE) and 9% lower mean absolute percentage error (MAPE) compared to benchmark models which includes both individual and hybrid ones. Additionally, the Diebold-Mariano (DM) test confirms the statistical significance of the model's improved performance. The results highlight the STL-LSTM hybrid model as a promising tool for agricultural price forecasting and provide a foundation for further advancements in this domain.

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Data Availability

The data supporting the findings of this study are available from the sources cited in the main text.

Code Availability

The code used for the analysis in this study is available from the corresponding author upon reasonable request.

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Acknowledgements

The authors sincerely thank the anonymous reviewers for their insightful feedback and constructive suggestions, which were invaluable in enhancing the quality of this paper.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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Authors and Affiliations

  1. ICAR-Central Institute of Temperate Horticulture, Srinagar, J&K, India

    Ronit Jaiswal

  2. ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India

    Girish K. Jha & Rajeev Ranjan Kumar

  3. Agriculture University, Jodhpur, Rajasthan, India

    Kapil Choudhary

Authors
  1. Ronit Jaiswal
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  2. Girish K. Jha
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Contributions

Ronit Jaiswal: Conceptualization, Methodology, Software, Validation, Formal Analysis, Investigation, Writing—Original Draft, Visualization. Girish Kumar Jha: Conceptualization, Validation, Formal Analysis, Investigation, Writing—Review & Editing, Supervision. Kapil Choudhary: Validation, Formal Analysis, Investigation, Writing—Review & Editing, Supervision. Rajeev Ranjan Kumar: Validation, Formal Analysis, Investigation, Writing—Review & Editing, Supervision.

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Correspondence to Girish K. Jha.

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The conducted research is not related to either human or animal use. All of the followed procedures were in accordance with the ethical and scientific standards. Authors declare that this manuscript is the result of their independent creation under the reviewers' comment and this work does not cause any harm to human or society.

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Jaiswal, R., Jha, G.K., Kumar, R.R. et al. STL-LSTM Hybrid Model for Forecasting Seasonal Agricultural Price Series. Ann. Data. Sci. (2025). https://doi.org/10.1007/s40745-025-00590-3

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