Bridging the Gap: Evaluating Traditional, Hybrid (Prophet), and Deep Learning Approaches in Time Series Forecasting

Seasonal forecasting is an essential forecasting branch with applications in finance, economics, supply chain management, energy, and healthcare.Forecasting is the foundation of an organization's planning, provides consistency with goals and objectives, and helps manage risks.Until the late 20th century, techniques such as ARIMA or exponential smoothing were the only commonly used techniques for time series forecasting.However, current hybrid methods like Facebook's Prophet and deep learning methods have been revealed to have the potential to improve the performance of the forecasting models.This paper aims to analyze and contrast the following approaches to forecasting: traditional methods, the Prophet forecasting method, and the performing deep-learning models, along with their advantages, disadvantages, and usage examples.The aim is to present, in a relatively brief and concise manner, the type of current state-of-the-art time series forecasting and, in the process, to also give pointers as to what one should look for when working on issues related to this field.