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Paper Abstract and Keywords
Presentation 2025-02-14 15:40
Demand forecast for daily necessities using time series analysis
Akira Ujiie, Hiroto Mishima (SIST), Isaya Takeuchi (Takeuchi Co., Ltd.), Atsushi Togashi (SIST), Shoichi Noguchi (SFAIS) SWIM2024-27
Abstract (in Japanese) (See Japanese page) 
(in English) With the advancement of AI technology, both statistical models and deep learning models have gained attention for time series data prediction. Statistical models, such as autoregressive and moving average models, are suitable for capturing linearity and seasonality in data. In contrast, deep learning models can learn nonlinear dependencies and complex data structures, making them capable of handling data characteristics that traditional statistical models cannot.
This paper constructs a sales prediction model using time series data prediction methods and compares the accuracy of statistical models and deep learning models. Using sales data from November 2020 to September 2024 provided by Takeuchi Corporation, the applicability of each model was verified. The evaluation used the root mean square error (RMSE) to propose the optimal method.
As a result, LSTM achieved the lowest RMSE, confirming its superiority in capturing nonlinearity and long-term dependencies. On the other hand, the moving average model could reduce random fluctuations and showed reasonable accuracy despite its simplicity. SARIMA, ARMA, and CNN models showed even lower accuracy. Overall, the high RMSE values indicated that the models did not achieve practical accuracy. It is believed that reducing data variability and incorporating external data (e.g., weather and event information) can further improve prediction accuracy.
Keyword (in Japanese) (See Japanese page) 
(in English) predictive models for time series data / ｓales forecasting / SARIMAX / LSTM / CNN / / /  
Reference Info. IEICE Tech. Rep., vol. 124, no. 371, SWIM2024-27, pp. 55-62, Feb. 2025.
Paper # SWIM2024-27 
Date of Issue 2025-02-07 (SWIM) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF SWIM2024-27

Conference Information
Committee SWIM  
Conference Date 2025-02-14 - 2025-02-14 
Place (in Japanese) (See Japanese page) 
Place (in English) Online (Zoom) 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Business Valuation and Reliability,Student Session,GeneralBusiness Valuation and Reliability,Student Session,GeneralBusiness Valuation and Reliability,Student Session,GeneralBusiness Valuation and Reliability,Student Session,General 
Paper Information
Registration To SWIM 
Conference Code 2025-02-SWIM 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Demand forecast for daily necessities using time series analysis 
Sub Title (in English)  
Keyword(1) predictive models for time series data  
Keyword(2) ｓales forecasting  
Keyword(3) SARIMAX  
Keyword(4) LSTM  
Keyword(5) CNN  
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Keyword(7)  
Keyword(8)  
1st Author's Name Akira Ujiie  
1st Author's Affiliation Shizuoka Institute of Science and Technology (SIST)
2nd Author's Name Hiroto Mishima  
2nd Author's Affiliation Shizuoka Institute of Science and Technology (SIST)
3rd Author's Name Isaya Takeuchi  
3rd Author's Affiliation Takeuchi Co., Ltd. (Takeuchi Co., Ltd.)
4th Author's Name Atsushi Togashi  
4th Author's Affiliation Shizuoka Institute of Science and Technology (SIST)
5th Author's Name Shoichi Noguchi  
5th Author's Affiliation Sendai Foundation for Applied Information Sciences (SFAIS)
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Speaker Author-1 
Date Time 2025-02-14 15:40:00 
Presentation Time 30 minutes 
Registration for SWIM 
Paper # SWIM2024-27 
Volume (vol) vol.124 
Number (no) no.371 
Page pp.55-62 
#Pages 8 
Date of Issue 2025-02-07 (SWIM) 

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