A Predictive Method for Temperature Based on Ensemble EMD with Linear Regression

• Published in: Algorithms Vol. 18; no. 8; p. 458
• Main Authors: Yang, Yujun, Yang, Yimei, Liao, Huijuan
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.08.2025
• Subjects: Accident prevention; Accuracy; Agricultural production; Climate; Climate change; Crop diseases; Data assimilation; Deep learning; Disease control; Economic forecasting; EEMD; Efficiency; Energy; Environmental protection; Food security; Food supply; LSTM; Machine learning; Meteorological research; Methods; Neural networks; Regression analysis; short-term forecast; Simulation; Statistical analysis; Statistical methods; Sustainable development; Temperature; Time series; time series predict; Urban planning; Weather; Weather forecasting; China
• ISSN: 1999-4893; 1999-4893
• DOI: 10.3390/a18080458

Temperature prediction plays a crucial role across various sectors, including agriculture and climate research. Understanding weather patterns, seasonal shifts, and climate dynamics heavily relies on accurate temperature forecasts. This paper presents an innovative hybrid method, EEMD-LR, that combines ensemble empirical mode decomposition (EEMD) with linear regression (LR) for temperature prediction. EEMD is used to decompose temperature signals into stable sub-signals, enhancing their predictability. LR is then applied to forecast each sub-signal, and the resulting predictions are integrated to obtain the final temperature forecast. The proposed EEMD-LR model achieved RMSE, MAE, and R2 values of 0.000027, 0.000021, and 1.000000, respectively, on the sine simulation time-series data used in this study. For actual temperature time-series data, the model achieved RMSE, MAE, and R2 values of 0.713150, 0.512700, and 0.994749, respectively. The experimental results on these two datasets indicate that the EEMD-LR model demonstrates superior predictive performance compared to alternative methods.