# Forecasting Daily Temperatures with Different Time Interval Data Using Deep Neural Networks
## 2 Related Work
### Neural Networks for Signal Processing  
Forecasting temperatures using numeric weather data with different time units can be viewed as signal processing because numeric weather data have sequences over time, and applications of signal processing using neural network models have used different types of input and target data. The main application areas of signal processing are speech recognition and bio-signal recognition, as well as time series forecasting [18; 19; 20; 21; 22; 23; 24; 25; 26; 27]. Researchers have used two typical neural network models for signal processing: recurrent neural networks (RNNs) and CNNs. RNNs are specialized for sequential data such as signal, time series, and text. These networks have a component called hidden state that is a function of all previous hidden states and that allows RNNs to perform well with sequential data. However, RNNs have a critical limitation, a vanishing gradient problem. The LSTM model is an advanced RNN; it includes the gates in RNN, and it addresses the vanishing gradient problem, allowing the model to perform much better than RNNs [28]. In the signal processing research with LSTM, [22] performed speech recognition tasks using deep LSTM, and that model outperformed those used in previous studies. Deep LSTM reflects the long term effectively through multiple levels of representation. In one study, [24] predicted seizures using LSTM to process electroencephalogram signal data. In another study, [27] used deep bidirectional LSTM network-based wavelet sequences to classify electrocardiogram signals, and their model showed better recognition performance than that of conventional networks. Researchers have used CNNs with success in computer vision, and they have performed well with speech recognition as well. CNNs have the key aspects of sparse interaction and parameter sharing using convolution layers with considerably fewer parameters than with MLP [29]. In research on signal processing using CNN, [21] performed speech recognition using CNN models, and their models outperformed the MLP models and hidden Markov model that had been used in previous studies. [19] used a CNN to process electrocardiogram data to predict various cardiovascular diseases, and others [23] used a CNN to process electroencephalogram signal data and diagnose epilepsy. In both of these studies, the researchers used CNNs with one-dimensional signal data; however, in a different study, [30] used two-dimensional electroencephalogram signal data, which enabled brain-computer interaction. Researchers have recently been studying both LSTM and CNNs to forecast time-series data. Ref. [7] used LSTM combining ensemble empirical mode decomposition and fuzzy entropy to forecast wind speed. Ref. [31] conducted and analyzed stable forecasting of environmental time series using LSTM or [3] employed CNN to process one-dimensional signal data to forecast monthly rainfall; in the latter study, the authors represented input data in one-dimensional signals using various weather factors. However, this method does not consider that the order of weather variables is independent. Furthermore, the methods mentioned above-employed input data and target data with the same time units. Most existing studies on temperature-related tasks using CNNs have been employed satellite data. Ref. [32] forecasted sea surface temperature field by processing satellite data with the combination of CNN and LSTM and [33] predicted subsurface temperatures by using CNN with satellite remote sensing data. In this study, we performed air temperature forecasting by applying the CNN to numeric weather data rather than satellite data with a suitable structure to process them. Most recently, various machine learning approaches, including multimodal learning which combines different types of inputs to perform specific tasks and multitask learning which learns related tasks simultaneously for computational efficiency and performance improvement by using shared information among related tasks, have been applied to the weather forecasting. Ref.