Humidity, as a key indicator in meteorology, is related to the production and sustainability of urban areas. This paper aims to forecast the humidity data of Shenyang City in 2024. We examined four explanatory variables closely related to humidity and developed a multiple  regression equation. Considering the mutual influence of these variables, we regard them as variable relations in multidimensional space and determine the weight of each factor by equation coefficients. On this basis, we further develop a smoothing index prediction model. The model incorporates a smoothing coefficient that varies over time and is flexibly adjusted based on actual data to accurately predict the humidity data at the next time point. The regression equation and time series model constructed in this paper comprehensively consider the factors affecting humidity from both spatial and temporal dimensions, aiming to enhance the accuracy and flexibility of the prediction results. This research has far-reaching significance for environmental monitoring, production, and daily life.

Linfeng Lou, Hua Xu. Humidity research based on multiple regression and time-varying exponential smoothing model. Academic Journal of Mathematical Sciences (2024) Vol. 5, Issue 2: 1-7. https://doi.org/10.25236/AJMS.2024.050201.

[1] Zuo Zhiyu, Mao Hanping, Zhang Xiaodong, Hu Jing, Han Greening, Ni Jing. Greenhouse temperature prediction model based on time series analysis[J]. Journal of Agricultural Machinery, 2010, 41(11): 173-177182

[2] Guo Qingchun, He Zhenfang, Hui Ying, Li Xue. Application of artificial neural network in relative humidity prediction [J]. Modern Food Science and Technology, 2013, 29(6):1297-1301

[3] Huang Tianyi, Wu Huarui, Zhu Huaji. A multimodal data-driven humidity prediction method for cucumber greenhouse [J]. Electronic Measurement Technology, 2023, 46(16):97-104

[4] Zhang Lei, Meng Kunying, Shen Xiajiong, et al. An MPI-parallelized three-dimensional neural network-based method for strong convective weather prediction: 202110832813

[5] Jia Pengqun, Li Jinghua. An immortal classic:The Clausius-Clapeyron equation after 190 years[J]. Advances in Meteorological Science and Technology, 2023, 13(2):2-4

[6] Li Zinai. Econometrics: Methods and Applications[M]. Beijing: Tsinghua University Press, 2020.

[7] Wang TX. Dynamic triple exponential smoothing method with dynamic smoothing coefficients and parameters for power generation forecasting in thermal power plants [J]. Power Equipment Management, 2024(1):150-152

[8] Zhao Tianze, Hu Xueyou, et al. Methanol price prediction based on adaptive exponential smoothing method [J]. Journal of Bengbu Institute, 2023, 2.

[9] Zhou Zhihua. Machine Learning[M]. Beijing. Tsinghua University Press. 2016.