An Improved iTransformer with RevIN and SSA for Greenhouse Soil Temperature Prediction DOI Creative Commons

Fahai Wang,

Yiqun Wang, Wenbai Chen

et al.

Agronomy, Journal Year: 2025, Volume and Issue: 15(1), P. 223 - 223

Published: Jan. 17, 2025

In contemporary agricultural practices, greenhouses serve as a critical component of infrastructure, where soil temperature plays vital role in enhancing pest management and regulating crop growth. However, achieving precise greenhouse environmental control continues to pose significant challenge. this context, the present study proposes ReSSA-iTransformer, an advanced predictive model engineered accurately forecast temperatures within across diverse temporal scales, encompassing both long-term short-term horizons. This capitalizes on iTransformer time-series forecasting framework integrates Singular Spectrum Analysis (SSA) decompose variables, thereby augmenting extraction pivotal features, such temperature. Furthermore, mitigate prevalent distribution shift issues inherent data, Reversible Instance Normalization (RevIN) is incorporated architecture. ReSSA-iTransformer adept at executing multi-step forecasts for extended immediate future intervals, offering comprehensive capabilities. Empirical evaluations substantiate that surpasses conventional models, including LSTM, Informer, Autoformer, all assessed metrics. Specifically, it attained R2 coefficients 98.51%, 97.03%, 97.26%, 94.83%, alongside MAE values 0.271, 0.501, 0.648, 1.633 predictions 3 h, 6 24 48 h respectively. These results highlight model’s superior accuracy robustness. Ultimately, not only provides dependable but also delivers actionable insights, facilitating enhanced practices.

Language: Английский

An Improved iTransformer with RevIN and SSA for Greenhouse Soil Temperature Prediction DOI Creative Commons

Fahai Wang,

Yiqun Wang, Wenbai Chen

et al.

Agronomy, Journal Year: 2025, Volume and Issue: 15(1), P. 223 - 223

Published: Jan. 17, 2025

In contemporary agricultural practices, greenhouses serve as a critical component of infrastructure, where soil temperature plays vital role in enhancing pest management and regulating crop growth. However, achieving precise greenhouse environmental control continues to pose significant challenge. this context, the present study proposes ReSSA-iTransformer, an advanced predictive model engineered accurately forecast temperatures within across diverse temporal scales, encompassing both long-term short-term horizons. This capitalizes on iTransformer time-series forecasting framework integrates Singular Spectrum Analysis (SSA) decompose variables, thereby augmenting extraction pivotal features, such temperature. Furthermore, mitigate prevalent distribution shift issues inherent data, Reversible Instance Normalization (RevIN) is incorporated architecture. ReSSA-iTransformer adept at executing multi-step forecasts for extended immediate future intervals, offering comprehensive capabilities. Empirical evaluations substantiate that surpasses conventional models, including LSTM, Informer, Autoformer, all assessed metrics. Specifically, it attained R2 coefficients 98.51%, 97.03%, 97.26%, 94.83%, alongside MAE values 0.271, 0.501, 0.648, 1.633 predictions 3 h, 6 24 48 h respectively. These results highlight model’s superior accuracy robustness. Ultimately, not only provides dependable but also delivers actionable insights, facilitating enhanced practices.

Language: Английский

Citations

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