# Application of Facebook's Prophet Algorithm for Successful Sales Forecasting Based on Real-world Data

arXiv: https://arxiv.org/abs/2005.07575 · HTML: https://mlsanity.com/paper/2005.07575/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 May 2020.

## Built on by (most cited first, 4 of 4)

- [Interpretable Multivariate Time Series Forecasting Using Neural Fourier Transform](https://mlsanity.com/paper/2405.13812/) (2024)
- [DNN-ForwardTesting: A New Trading Strategy Validation using Statistical Timeseries Analysis and Deep Neural Networks](https://mlsanity.com/paper/2210.11532/) (2022)
- [Comparison Analysis of Facebook's Prophet, Amazon's DeepAR+ and CNN-QR Algorithms for Successful Real-World Sales Forecasting](https://mlsanity.com/paper/2105.00694/) (2021)
- [News-Driven Stock Price Forecasting in Indian Markets: A Comparative Study of Advanced Deep Learning Models](https://mlsanity.com/paper/2411.05788/) (2024)

## Abstract (arXiv)

This paper presents a framework capable of accurately forecasting future sales in the retail industry and classifying the product portfolio according to the expected level of forecasting reliability. The proposed framework, that would be of great use for any company operating in the retail industry, is based on Facebook's Prophet algorithm and backtesting strategy. Real-world sales forecasting benchmark data obtained experimentally in a production environment in one of the biggest retail companies in Bosnia and Herzegovina is used to evaluate the framework and demonstrate its capabilities in a real-world use case scenario.