A Novel Approach for Temperature Forecasting in Climate Change Using Ensemble Decomposition of Time Series

被引:0
|
作者
Estrada-Patino, Erick [1 ]
Castilla-Valdez, Guadalupe [1 ]
Frausto-Solis, Juan [1 ]
Gonzalez-Barbosa, Javier [1 ]
Sanchez-Hernandez, Juan Paulo [2 ]
机构
[1] Inst Tecnol Ciudad Madero, Tecnol Nacl Mexico, Grad & Res Div, Ciudad Madero 89440, Mexico
[2] Univ Politecn Estado Morelos, Informat Technol Div, Blvd Cuauhnahuac 566, Jiutepec 62574, Mexico
关键词
Singular value decomposition; Time series forecasting; Ensemble time series; Deep learning; Time series decomposition; Climate change; NOISE-REDUCTION; PREDICTION;
D O I
10.1007/s44196-024-00667-6
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This paper presents FORSEER (Forecasting by Selective Ensemble Estimation and Reconstruction), a novel methodology designed to address temperature forecasting under the challenges inherent to climate change. FORSEER integrates decomposition, forecasting, and ensemble methods within a modular framework. This methodology decomposes the time series into trend, seasonal, and residual components. Subsequently, multiple optimized forecast models are applied to each component. These component models are then carefully weighted and combined through an ensemble process to generate a final robust forecast. Experimental results demonstrate that FORSEER is an efficient computational forecasting methodology for complex climate time series. Furthermore, we show that FORSEER has an equivalent forecasting performance to the M4 competition champion SMYL method for temperature series. Besides, the proposed methodology has less computational complexity than SMYL, making it a more accessible and scalable option. FORSEER's modular architecture also allows flexibility when substituting techniques depending on the context of the problem, facilitating the parallel execution of independent tasks and resulting in a strategy adaptable to multiple contexts.
引用
收藏
页数:18
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