  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.
