论文标题
温度预测的Arima和深度学习模型之间的比较
Comparison between ARIMA and Deep Learning Models for Temperature Forecasting
论文作者
论文摘要
天气预报以各种方式使我们受益，从耕种的农民和收获农作物到航空公司以安排航班。由于气氛的混乱性，天气预报是一项艰巨的任务。因此，引起了很多研究的关注，以获得好处并克服天气预报的挑战。本文将Arima（自动回归整合运动平均值）和深度学习模型与预测温度进行了比较。深度学习模型由一维卷积层组成，以提取空间特征和LSTM层以提取时间特征。这两个模型都应用于饥饿的szeged，饥饿的小时温度数据集。根据实验结果，深度学习模型的性能比传统的Arima方法更好。
Weather forecasting benefits us in various ways from farmers in cultivation and harvesting their crops to airlines to schedule their flights. Weather forecasting is a challenging task due to the chaotic nature of the atmosphere. Therefore lot of research attention has drawn to obtain the benefits and to overcome the challenges of weather forecasting. This paper compares ARIMA (Auto Regressive Integrated Moving Average) model and deep learning models to forecast temperature. The deep learning model consists of one dimensional convolutional layers to extract spatial features and LSTM layers to extract temporal features. Both of these models are applied to hourly temperature data set from Szeged, Hungry. According to the experimental results deep learning model was able to perform better than the traditional ARIMA methodology.
