poster

Incorporating seasonal time series analysis with search behavior information in sales forecasting

Authors:

Shaoping MaAuthors Info & Claims

WWW '12 Companion: Proceedings of the 21st International Conference on World Wide Web

Pages 615 - 616

https://doi.org/10.1145/2187980.2188154

Published: 16 April 2012 Publication History

Get Access

Abstract

We consider the problem of predicting monthly auto sales in mainland China. First, we design an algorithm using click-through and query reformulation information to cluster related queries and count their frequencies on monthly-basis. By introducing Exponentially Weighted Moving Averages (EWMA) model, we measure the seasonal impact on the sales trend. Two features are combined using linear regression. The experiment shows that our model is effective with high accuracy and outperforms conventional forecasting models.¹

References

[1]

H. Choi and H. Varian. Predicting the present with google trends. Technical report, Google Inc., 2009.

Google Scholar

[2]

E. Sadikov, J. Madhavan, L. Wang, and A. Halevy. Clustering query refinements by user intent. In Proceedings of the 19th World Wide Web Conference, 2010.

Digital Library

Google Scholar

[3]

Peter R. Winters. Forecasting sales by exponentially weighted moving averages. Management Science, Vol.6(No.3):pp. 324--342, April 1960.

Digital Library

Google Scholar

Cited By

View all

Gandhi MKarimli Maharram VRaja GSellapaandi SRathor KSingh K(2023)A Novel Method for Exploring the Store Sales Forecasting using Fuzzy Pruning LS-SVM Approach2023 2nd International Conference on Edge Computing and Applications (ICECAA)10.1109/ICECAA58104.2023.10212292(537-543)Online publication date: 19-Jul-2023
https://doi.org/10.1109/ICECAA58104.2023.10212292
Krupenkin MHill SRothschild D(2023)Do Partisans Make Different Investment Decisions When Their Party is in Power?Political Behavior10.1007/s11109-023-09883-w46:3(1535-1561)Online publication date: 17-Jul-2023
https://doi.org/10.1007/s11109-023-09883-w
Kavitha MSrinivasan RKavitha RSuganthy M(2023)Sales Demand Forecasting for Retail Marketing Using XGBoost AlgorithmIntelligent and Soft Computing Systems for Green Energy10.1002/9781394167524.ch9(127-140)Online publication date: 11-May-2023
https://doi.org/10.1002/9781394167524.ch9
Show More Cited By

Index Terms

Incorporating seasonal time series analysis with search behavior information in sales forecasting
1. Information systems
  1. Information retrieval

Recommendations

Sales forecasting for Chemical Products by Using SARIMA Model
ICBDE '22: Proceedings of the 5th International Conference on Big Data and Education

Sales forecasting is widely used in enterprise resource management, which provides valuable information for efficient management. Sales forecasting facilitates the company to produce and stock products on demand. Based on the analysis of time series, ...
Time series forecasting by a seasonal support vector regression model

The support vector regression (SVR) model is a novel forecasting approach and has been successfully used to solve time series problems. However, the applications of SVR models in a seasonal time series forecasting has not been widely investigated. This ...
A comparison of multivariate and univariate time series approaches to modelling and forecasting emergency department demand in Western Australia

The model identification process for VARMA, ARMA and Winters method.Display Omitted VARMA, ARMA and Winters methods are used extensively for planning and management.Multivariate VARMA model is a reliable tool for predicting ED demand by category.It ...

Comments

Information & Contributors

Information

Published In

WWW '12 Companion: Proceedings of the 21st International Conference on World Wide Web

April 2012

1250 pages

ISBN:9781450312301

DOI:10.1145/2187980

General Chairs:
Alain Mille
Université de Lyon, France
,
Fabien Gandon
INRIA, France
,
Jacques Misselis
HP, France
,
Program Chairs:
Michael Rabinovich
Case Western Reserve University, USA
,
Steffen Staab
University of Koblenz-Landau, Germany

In-Cooperation

SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 April 2012

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Poster

Conference

WWW 2012

Sponsor:

Univ. de Lyon

WWW 2012: 21st World Wide Web Conference 2012

April 16 - 20, 2012

Lyon, France

Acceptance Rates

Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

6
Total Citations
View Citations
239
Total Downloads

Downloads (Last 12 months)4
Downloads (Last 6 weeks)0

Reflects downloads up to 27 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Gandhi MKarimli Maharram VRaja GSellapaandi SRathor KSingh K(2023)A Novel Method for Exploring the Store Sales Forecasting using Fuzzy Pruning LS-SVM Approach2023 2nd International Conference on Edge Computing and Applications (ICECAA)10.1109/ICECAA58104.2023.10212292(537-543)Online publication date: 19-Jul-2023
https://doi.org/10.1109/ICECAA58104.2023.10212292
Krupenkin MHill SRothschild D(2023)Do Partisans Make Different Investment Decisions When Their Party is in Power?Political Behavior10.1007/s11109-023-09883-w46:3(1535-1561)Online publication date: 17-Jul-2023
https://doi.org/10.1007/s11109-023-09883-w
Kavitha MSrinivasan RKavitha RSuganthy M(2023)Sales Demand Forecasting for Retail Marketing Using XGBoost AlgorithmIntelligent and Soft Computing Systems for Green Energy10.1002/9781394167524.ch9(127-140)Online publication date: 11-May-2023
https://doi.org/10.1002/9781394167524.ch9
Khan MSaqib SAlyas TUr Rehman ASaeed YZeb AZareei MMohamed E(2020)Effective Demand Forecasting Model Using Business Intelligence Empowered With Machine LearningIEEE Access10.1109/ACCESS.2020.30037908(116013-116023)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.3003790
CATAL CECE KArslan BAkbulut A(2019)Benchmarking of Regression Algorithms and Time Series Analysis Techniques for Sales ForecastingBalkan Journal of Electrical and Computer Engineering10.17694/bajece.494920(20-26)Online publication date: 31-Jan-2019
https://doi.org/10.17694/bajece.494920
Tsai KWang YKuo HTsai JChang CHung HShuai H(2017)Multi-Source Learning for Sales Prediction2017 Conference on Technologies and Applications of Artificial Intelligence (TAAI)10.1109/TAAI.2017.38(148-153)Online publication date: Dec-2017
https://doi.org/10.1109/TAAI.2017.38

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Abstract

References

Cited By

Index Terms

Recommendations

Sales forecasting for Chemical Products by Using SARIMA Model

Time series forecasting by a seasonal support vector regression model

A comparison of multivariate and univariate time series approaches to modelling and forecasting emergency department demand in Western Australia

Comments

Information

Published In

Sponsors

In-Cooperation

Publisher

Publication History

Permissions

Check for updates

Author Tags

Qualifiers

Conference

Acceptance Rates

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

Login options

Full Access

View options

PDF

eReader

Share

Share this Publication link

Share on social media

Affiliations