# Forecasting Daily Temperatures with Different Time Interval Data Using Deep Neural Networks
###### Abstract  
Temperature forecasting has been a consistent research topic owing to its significant effect on daily lives and various industries. However, it is an ever-challenging task because temperature is affected by various climate factors. Research on temperature forecasting has taken one of two directions: time-series analysis and machine learning algorithms. Recently, a large amount of high-frequent climate data have been well-stored and become available. In this study, we apply three types of neural networks, multilayer perceptron, recurrent, and convolutional, to daily average, minimum, and maximum temperature forecasting with higher-frequency input features than researchers used in previous studies. Applying these neural networks to the observed data from three locations with different climate characteristics, we show that prediction performance with highly frequent hourly input data is better than forecasting performance with less-frequent daily inputs. We observe that a convolutional neural network, which has been mostly employed for processing satellite images rather than numeric weather data for temperature forecasting, outperforms the other models. In addition, we combine state of the art weather forecasting techniques with the convolutional neural network and evaluate their effects on the temperature forecasting performances. convolution neural network; deep learning; long short term memory; temperature forecasting +
Footnote †: journal: Medical  
work. Contrary, the physics-based methods have the advantage of being interpretable and having the potential for extrapolating over observed conditions. However, it has the disadvantage of requiring well-defined prior knowledge and a large computational capacity [13; 14]. Recently, data-driven models for weather forecasting have been more actively studied with the increase in the number of available features and observations. To overcome these challenges, research has increased steadily on temperature forecasting, in two main directions. One is that researchers have used time-series analysis based on autoregressive integrated moving average (ARIMA) to study longer-term predictions such as monthly and annual [15; 16; 17]. The other is machine learning algorithms based on neural network models for shorter-term predictions such as hourly and daily. Existing research has mostly been focused on forecasting temperatures using consistent time unit data. Specifically, both the input data and the target data are the same time unit, for example, using daily input data to forecast day-ahead temperature. However, as the amount of available high-frequent data has increased and computing power using graphics processing units has improved, it is possible to use hourly input data for daily temperature forecasting, as opposed to only the daily input data that could previously be used. Both hourly and daily patterns can be employed to forecast daily temperatures, but because these data are abundant and detailed, it is essential to process them efficiently and effectively. In this context, neural network models have powerful versatility to process large amounts of more detailed data. In this study, we forecast daily average, minimum, and maximum temperatures with different time-interval data using deep neural network models. Our major contributions are as follows. First, unlike in previous studies, we used detailed time unit input data rather than target data to forecast day-ahead temperature. Second, to enable efficient and effective processing of more detailed and abundant data, we used three neural network models: multilayer perceptron (MLP), long-short term memory (LSTM), and convolution neural network (CNN). In particular, we applied a CNN, which has been mostly employed for satellite data in previous studies, to numeric weather data with a suitable structure for them, and we, in fact, found that it outperformed the other models. Third, we compared the models for factors such as time unit of input data, time length of input data, regions, and neural network models.