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- Title
A Seasonal-Trend Decomposition and Single Dendrite Neuron-Based Predicting Model for Greenhouse Time Series.
- Authors
Li, Qianqian; He, Houtian; Xue, Chenxi; Liu, Tongyan; Gao, Shangce
- Abstract
The greenhouse farming always uses sensors to monitor the dynamic climate parameters and generate time-related data. The prediction of these time series contributes a lot to greenhouse cultivation. Plenty of works concentrate on the chaotic characteristics of the time series and propose many machine learning-based models. However, the intrinsic features of them are ignored, i.e., seasonality and tendency. In this study, we propose a novel predicting model SDN that utilizes the Seasonal-trend Decomposition as preprocessing method and the Single Dendrite Neuron as nonlinear fitter to tackle greenhouse time series predictions. The decomposition gives SDN a flexibility that can process each component separately, while the well-designed neuron structure provides SDN with time efficiency. Accordingly, the experimental results show that the proposed SDN not only beats the widely used machine learning-based models, but also shows the robustness considering customized parameters and outliers in datasets, which enhance the possibility for SDN to be employed in the practical usage scenarios.
- Subjects
DENDRITES; TIME series analysis; AGRICULTURE; GREENHOUSES; DECOMPOSITION method
- Publication
Environmental Modeling & Assessment, 2024, Vol 29, Issue 3, p427
- ISSN
1420-2026
- Publication type
Article
- DOI
10.1007/s10666-023-09931-z