PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
September 27, 2025International Journal of Innovative Research and Scientific StudiesOpen Access

Data-driven forecasting of sales influenced by climate variability using deep learning

View Full Paper
Ask AI
Bookmark
Share

Authors

SKSiriwan KajornkasiratCLChayanin LimrattanabunchongNSNattaseth Sriklin

Discussion

Loading...

Member takes

Overview

Analysis shows regression models outperform deep learning in sales forecasting accuracy, emphasizing climate's impact.

Key Points

  • Regression-based models demonstrated superior predictive power compared to deep learning and time series models.
  • Weather conditions, including humidity and temperature, showed moderate correlations with sales volume.
  • An integrated information system can improve data accessibility and reduce redundancy in sales forecasting efforts.
  • The framework offers a scalable solution, minimizing reliance on third-party business intelligence tools.

Cite This Study

Kajornkasirat et al. (2025) studied this question.

synapsesocial.com/papers/68d7be6ceebfec0fc5238316https://doi.org/10.53894/ijirss.v8i6.10223
View Full Paper
Ask AI
Bookmark
Share

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1A machine learning framework for predicting weather impact on retail sales2024 · 4 citations
  2. 2Sales forecasting for retail stores using hybrid neural networks and sales-affecting variables2025 · 12 citations
  3. 3Addressing Seasonality and Trend Detection in Predictive Sales Forecasting: A Machine Learning Perspective2024 · 14 citations
  4. 4Time Series Sales Forecasting: A Hybrid Deep Learning Regularization Approach2024 · 3 citations
  5. 5SEASONALITY AND TREND IN SALES FORECASTING2026