Using time series analysis to study the cumulative distribution function of meteorological data and selecting typical meteorological cycles for normalized modeling

Short-term prediction of meteorological data needs to extract effective information from complex timeseries features, which centers on improving data quality through preprocessing, constructing prediction models adapted to seasonal variations, and optimizing fuzzy delineation of data distributions in order to improve prediction accuracy.Based on the hour-by-hour meteorological data of an international airport in China, this study proposes a time series analysis framework that integrates data cleaning, normalization, seasonal ARIMA modeling, and cumulative distribution domain delineation, aiming to improve the accuracy of short-term meteorological forecasts.For the missing values and outliers in the raw data, the segmented linear interpolation and truncation strategies are used to reconstruct the features, and the seasonal segmentation strategies of cold, hot, and transitional seasons are combined to enhance the data integrity.The min-max normalization is used to eliminate the differences in the magnitudes of multiple sources of meteorological elements, and a seasonal ARIMA product model is constructed to capture the cyclical fluctuation pattern of the temperature data.The cumulative probability distribution method is further introduced to divide the thesis domain, and the temperature data are mapped into interpretable fuzzy intervals to optimize the model's ability to express uncertainty.The experimental results show that the method in this paper significantly outperforms the traditional RNN and LSTM models in the wind speed and temperature prediction task, in which the MAE, MSE, and MAPE of the daily maximum temperature prediction are reduced to 0.0554, 0.00604, and 20.13%, respectively, which verifies the model's utility in complex meteorological time-series features.