Research on World Agricultural Economy

Time Series Analysis and Optimization of the Prediction Model of Agricultural Insurance Loss Ratio

Yu Wang

Faculty of Economics and Business, Universiti Malaysia Sarawak (UNIMAS), Kota Samarahan, Sarawak 94300, Malaysia

Muhammad Asraf bin Abdullah

Faculty of Economics and Business, Universiti Malaysia Sarawak (UNIMAS), Kota Samarahan, Sarawak, 94300, Malaysia

Josephine Yau Tan Hwang

Faculty of Economics and Business, Universiti Malaysia Sarawak (UNIMAS), Kota Samarahan, Sarawak 94300, Malaysia

DOI: https://doi.org/10.36956/rwae.v5i4.1219

Received: 6 August 2024 | Revised: 5 September 2024 | Accepted: 6 September 2024 | Published Online: 7 November 2024

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Abstract

For ensuring successful financial planning to perform sustainable farming, one key sector is to provide solutions that could accurately predict the agricultural loss ratios. In China, the Henan province is considered to be an agricultural center that is primarily exposed to drastic weather fluctuations that directly impact the crop yields. This study was conducted in Henan province from January 2020 to December 2023. With the data collected from that period, the study proposes a combinatory model combining Deep Gaussian Processes with Bayesian Long Short-Term Memory (LSTM) networks. The model was trained on data encompassing weather conditions, agricultural practices, and historical insurance claims. The experimental analysis was conducted against other traditional models, including ARIMA and Support Vector Regression. The RMSE improvement of the proposed model was around 7.2% on training data and 8.2% on test data, which demonstrates enhanced predictive accuracy. The enhanced performance of the proposed model was reflected in its effectiveness in reducing log-likelihood errors across training epochs. The model had demonstrated better robustness in handling complex and multi-dimensional agricultural data.

Keywords: Agriculture; Bayesian LSTM; ARIMA; Support Vector Regression; Loss Ratio; Log‑Likelihood Errors

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