An Approach to Weather Forecasting using Additive and Deep Learning Algorithms

Abstract

Weather is an important factor that directly affects farming activities. The temperature and humidity of a region play a crucial role in the type of crop to be cultivated. Thus, It becomes important for farmers to know the future trends in the weather to plan farming activities. There are several classical weather prediction services but all of them depend on complex modelling and are operated by government authorities. In this case, time series becomes an effective way of forecasting these temperature trends as it not only requires minimal computational resources but only the past weather data to achieve results. In this paper, we study the process of implementing both additive and regression-based models and compare their performance to decide which approach is the best for weather prediction.