文章摘要
赵成兵,刘丹秀,谢新平,刘静.基于时间序列的季节性气温预测研究[J].安徽建筑大学学报,2022,30(3):83-89
基于时间序列的季节性气温预测研究
Research on Seasonal Temperature Forecasting Based on Time Series
  
DOI:
中文关键词: 随机森林  自回归移动平均模型  平均温度  季节性  Python
英文关键词: random forest  autoregressive integrated moving average  average temperature  seasonality  Python
基金项目:安徽省高等学校自然科学基金项目(KJ2021A0631)
作者单位
赵成兵 安徽建筑大学  数理学院安徽  合肥  230601 
刘丹秀 安徽建筑大学  数理学院安徽  合肥  230601 
谢新平 安徽建筑大学  数理学院安徽  合肥  230601 
刘静 安徽建筑大学  数理学院安徽  合肥  230601 
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中文摘要:
      气温的变化受风速、湿度、日照时数等因素的影响,可以通过分析这些因素预测气温的变化情况。考虑到气温序列中存在季节特性,采用 One-Hot 编码方法提取气温序列中的季节性信息,并作为随机森林模型的输入特征,对月平均气温进行拟合与预测。由于模型构建时涉及众多超参数,文中利用随机搜索和网格搜索两种算法优化模型中的超参数。结果表明:考虑季节性的随机森林模型拟合效果优于简单随机森林模型,预测数据变化趋势与实际观测基本一致,拟合精度可以达到 96.14%。经两种方法对超参数寻优之后,模型拟合精度可以达到 96.45%。
英文摘要:
      The variation of temperature is influenced by factors including wind speed,humidity,sunshine duration,which canbe analyzed to predict the temperature. Considering the seasonal feature in the temperature series,the seasonal information in thetemperature series was extracted with the One-Hot encoding method and used as the input variable of the random forest model to fit andforecast the monthly average temperature. Since numerous hyperparameters are involved in the model,random search algorithm and gridsearch algorithm are used to optimize the hyperparameters. The results show that the fitting effect of the random forest model consideringseasonality is better than that of the simple random forest model,and the predicted data is basically consistent with the actual situation,with the fitting accuracy reaching 96.14%. The fitting accuracy can be up to 96.45% after the hyperparameters search by both methods.
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