Sales Prediction Through Time Series Analysis with Machine Learning

Kamal Kumar, Reena Devi, Pardeep Goel

2023

Abstract

In this article, we examine how machine learning models are used in sales predictive analytics. This paper’s primary goal is to investigate the key methods and research methods of applying machine learning to sales forecasting. It has been thought about how machine learning (ML) generalization would affect things. Such result may be used to forecast sales whenever there is just a little quantity of past records for a certain sales time series, such as after the opening of a new store or product. Researchers have researched to create regression groups by overlaying individual models. The results suggest that stack tactics can enhance the prognostic accuracy of predictive methods aimed at selling period series forecasts.

Paper Citation

in Harvard Style

Kumar K., Devi R. and Goel P. (2023). Sales Prediction Through Time Series Analysis with Machine Learning. In Proceedings of the 1st International Conference on Artificial Intelligence for Internet of Things: Accelerating Innovation in Industry and Consumer Electronics - Volume 1: AI4IoT; ISBN 978-989-758-661-3, SciTePress, pages 163-169. DOI: 10.5220/0012608500003739

in Bibtex Style

@conference{ai4iot23,
author={Kamal Kumar and Reena Devi and Pardeep Goel},
title={Sales Prediction Through Time Series Analysis with Machine Learning},
booktitle={Proceedings of the 1st International Conference on Artificial Intelligence for Internet of Things: Accelerating Innovation in Industry and Consumer Electronics - Volume 1: AI4IoT},
year={2023},
pages={163-169},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012608500003739},
isbn={978-989-758-661-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Artificial Intelligence for Internet of Things: Accelerating Innovation in Industry and Consumer Electronics - Volume 1: AI4IoT
TI - Sales Prediction Through Time Series Analysis with Machine Learning
SN - 978-989-758-661-3
AU - Kumar K.
AU - Devi R.
AU - Goel P.
PY - 2023
SP - 163
EP - 169
DO - 10.5220/0012608500003739
PB - SciTePress