Table of Contents

• Introduction
• ARIMA in a Nutshell
• Key Assumptions and Limitations of the ARIMA Model
• Forecasting Process in Python: Seasonal ARIMA vs. Facebook Prophet
• Performance Comparison Between Two Models
• Conclusion

Introduction

Time Series Forecasting has long been a vital tool for making predictions in businesses, especially before the rise of Machine Learning (ML). It plays an important role in areas such as resource allocation, inventory management, and budget planning. Often, forecasting is described as a blend of art and science because it requires both technical understanding and human intuition and judgement.

Among time series forecasting techniques, ARIMA (Auto Regressive Integrated Moving Average) has been one of the most popular methods, originally developed by George Box and Gwilym Jenkins in 1970. However, despite its popularity, ARIMA has certain drawbacks. This article aims to compare the performance of ARIMA with the Prophet model, developed by Facebook, while highlighting the…