 |
International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
|
Issue: February 2026
Volume 11 | Issue 2
Review Result and Publication of Paper within : 2-3 days
Click Here For more DetailsFor Authors
Forms / Download
Published Issue Details
Editorial Board
Other IMP Links
Facts & Figure
Impact Factor : 8.14
Issue per Year : 12
Volume Published : 11
Issue Published : 117
Article Submitted : 21307
Article Published : 8476
Total Authors : 22301
Total Reviewer : 802
Total Countries : 156
Indexing Partner
Licence
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
|
Published Paper Details
|
|
| Paper Title: | Sales Forecasting for Rossmann Store using GRU with Weather Features |
| Authors Name: | M VIJAYA KUMAR , LANKE GAYATHRI , DEVARAMPATI SNITHA GRACE , SHAIK ARSHAD , VENKATA GANESH KUMAR DEGALA |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJRTI_205994
|
| Published Paper Id: | IJRTI2509011 |
| Published In: | Volume 10 Issue 9, September-2025 |
| DOI: | |
| Abstract: | Sales forecasting plays a vital role in ensuring the success and efficiency of retail businesses. This project focuses on predicting the daily sales of Rossmann stores using a Gated Recurrent Unit (GRU)-based deep learning model. The dataset used comprises multiple years of historical sales data along with store information, promotions, holidays, and weather features. The inclusion of weather features serves to improve the forecasting accuracy, as external factors like temperature, precipitation, and wind speed can influence customer footfall and purchasing behavior. The GRU model was chosen for its ability to handle sequential data effectively and capture long-term dependencies. Grid Search was employed to fine-tune the hyperparameters and enhance the performance of the model. The model was trained and evaluated using Root Mean Square Error (RMSE) as the performance metric. Results demonstrate that incorporating weather features into the GRU-based model significantly improves forecasting performance, making it a practical solution for demand prediction in the retail sector. |
| Keywords: | Sales Forecasting, Rossmann Store, Gated Recurrent Unit, Deep Learning, Weather Features, Grid Search, RMSE, Time Series Prediction, Retail Analytics, Demand Forecasting. |
| Cite Article: | "Sales Forecasting for Rossmann Store using GRU with Weather Features", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 9, page no.a103-a107, September-2025, Available :http://www.ijrti.org/papers/IJRTI2509011.pdf |
| Downloads: | 0002616 |
| ISSN: |
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
| Publication Details: |
Published Paper ID: IJRTI2509011
Registration ID:205994
Published In: Volume 10 Issue 9, September-2025
DOI (Digital Object Identifier):
Page No: a103-a107 Country: HYDERABAD, Telangana, India Research Area: Computer Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2509011 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2509011 |
| Share Article: | |
|
Click Here to Download This Article |
|
| Article Preview | |
|
|
|
Major Indexing from www.ijrti.org
| Google Scholar | ResearcherID Thomson Reuters | Mendeley : reference manager | Academia.edu |
| arXiv.org : cornell university library | Research Gate | CiteSeerX | DOAJ : Directory of Open Access Journals |
| DRJI | Index Copernicus International | Scribd | DocStoc |
ISSN Details
 |
 |
|
ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
DOI (A digital object identifier)
| Providing A digital object identifier by DOI.ONE How to Get DOI? |
Conference
Open Access License Policy
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Important Details
Join RMS/Earn 300
WhatsApp
Click Here
Click Here
Indexing Partner |
|||
| Copyright © 2026 - All Rights Reserved - IJRTI | |||