complex-sales-forecasting
Exploring the efficacy of statistical and econometric methodologies for sales forecasting, this repository provides a comprehensive analysis alongside code implementations, offering empirical insights to guide decision-making in the retail industry.
https://github.com/oleksandrkosovan/complex-sales-forecasting
Science Score: 67.0%
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✓DOI references
Found 6 DOI reference(s) in README -
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○Scientific vocabulary similarity
Low similarity (8.3%) to scientific vocabulary
Keywords
Repository
Exploring the efficacy of statistical and econometric methodologies for sales forecasting, this repository provides a comprehensive analysis alongside code implementations, offering empirical insights to guide decision-making in the retail industry.
Basic Info
- Host: GitHub
- Owner: OleksandrKosovan
- License: gpl-3.0
- Language: Jupyter Notebook
- Default Branch: main
- Homepage: https://doi.org/10.1007/978-3-031-54820-8_27
- Size: 27.6 MB
Statistics
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 0
Topics
Metadata Files
README.md
Complex Comparison of Statistical and Econometrics Methods for Sales Forecasting
Sales forecasting is critical for decision-making in the retail industry. This study delves into the contemporary landscape of sales forecasting methods, aiming to empirically assess the performance of various statistical and econometric models. Through rigorous evaluation across diverse datasets, we aim to identify stable methods with reliable predictive capabilities. Our research contributes by establishing baseline models that offer trustworthy forecasts, thus guiding practical applications and future research efforts. The paper meticulously details the study’s methodology, results, and discussions, providing a comprehensive understanding of the strengths, limitations, and implications of the evaluated forecasting methods.
Kosovan, O., Datsko, M. (2024). Complex Comparison of Statistical and Econometrics Methods for Sales Forecasting. In: Silhavy, R., Silhavy, P. (eds) Data Analytics in System Engineering. CoMeSySo 2023. Lecture Notes in Networks and Systems, vol 935. Springer, Cham. 
Data Sets
- M5 Forecasting. URL
- Hack4Retail by Fozzy Group. URL
- Corporación Favorita Grocery Sales Forecasting. URL
Citation
If you use the findings or code from this repository in your research, please consider citing our paper:
Kosovan, O., Datsko, M. (2024). Complex Comparison of Statistical and Econometrics Methods for Sales Forecasting.
In: Silhavy, R., Silhavy, P. (eds) Data Analytics in System Engineering. CoMeSySo 2023.
Lecture Notes in Networks and Systems, vol 935. Springer, Cham. https://doi.org/10.1007/978-3-031-54820-8_27
Owner
- Name: Oleksandr Kosovan
- Login: OleksandrKosovan
- Kind: user
- Location: Lviv, Ukraine
- Website: https://oleksandrkosovan.github.io
- Repositories: 2
- Profile: https://github.com/OleksandrKosovan
Data Scientist/Machine Learning Engineer
Citation (CITATION.cff)
cff-version: 1.2.0 message: "If you use this software, please cite it as below." authors: - family-names: "Kosovan" given-names: "Oleksandr" - family-names: "Datsko" given-names: "Myroslav" title: "Complex Comparison of Statistical and Econometrics Methods for Sales Forecasting" version: 1.0.0 doi: 10.1007/978-3-031-54820-8_27 date-released: 2024-02-24 url: "https://github.com/OleksandrKosovan/complex-sales-forecasting"