Forecasting Demand with limited information using gradient tree boosting

Stephan Chang; Felipe Meneguzzi

Forecasting Demand with limited information using gradient tree boosting

Stephan Chang, Felipe Meneguzzi

Computing Science

Pontifícia Universidade Católica do Rio Grande do Sul

Research output: Chapter in Book/Report/Conference proceeding › Published conference contribution

Abstract

Demand forecasting is an important challenge for industries seeking to optimize service quality and expenditures. Generating accurate forecasts is difficult because it depends on the quality of the data available to train predictive models, as well as on the model chosen for the task. We evaluate the approach on two datasets of varying complexity and compare the results with three machine learning algorithms, Results show our approach can outperform these approaches.

Original language	English
Title of host publication	FLAIRS 2017 - Proceedings of the 30th International Florida Artificial Intelligence Research Society Conference
Editors	Vasile Rus, Zdravko Markov
Publisher	AAAI Press
Pages	227-230
Number of pages	4
ISBN (Electronic)	9781577357872
Publication status	Published - 2017
Event	30th International Florida Artificial Intelligence Research Society Conference, FLAIRS 2017 - Marco Island, United States Duration: 22 May 2017 → 24 May 2017

Conference

Conference	30th International Florida Artificial Intelligence Research Society Conference, FLAIRS 2017
Country/Territory	United States
City	Marco Island
Period	22/05/17 → 24/05/17

Bibliographical note

Publisher Copyright:
Copyright © 2017, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.

Cite this

Forecasting Demand with limited information using gradient tree boosting. / Chang, Stephan; Meneguzzi, Felipe.
FLAIRS 2017 - Proceedings of the 30th International Florida Artificial Intelligence Research Society Conference. ed. / Vasile Rus; Zdravko Markov. AAAI Press, 2017. p. 227-230.

Research output: Chapter in Book/Report/Conference proceeding › Published conference contribution

Chang, S & Meneguzzi, F 2017, Forecasting Demand with limited information using gradient tree boosting. in V Rus & Z Markov (eds), FLAIRS 2017 - Proceedings of the 30th International Florida Artificial Intelligence Research Society Conference. AAAI Press, pp. 227-230, 30th International Florida Artificial Intelligence Research Society Conference, FLAIRS 2017, Marco Island, United States, 22/05/17.

@inproceedings{c8dc78b54eab47a6b309a1776c0adb91,

title = "Forecasting Demand with limited information using gradient tree boosting",

abstract = "Demand forecasting is an important challenge for industries seeking to optimize service quality and expenditures. Generating accurate forecasts is difficult because it depends on the quality of the data available to train predictive models, as well as on the model chosen for the task. We evaluate the approach on two datasets of varying complexity and compare the results with three machine learning algorithms, Results show our approach can outperform these approaches.",

author = "Stephan Chang and Felipe Meneguzzi",

note = "Publisher Copyright: Copyright {\textcopyright} 2017, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.; 30th International Florida Artificial Intelligence Research Society Conference, FLAIRS 2017 ; Conference date: 22-05-2017 Through 24-05-2017",

year = "2017",

language = "English",

pages = "227--230",

editor = "Vasile Rus and Zdravko Markov",

booktitle = "FLAIRS 2017 - Proceedings of the 30th International Florida Artificial Intelligence Research Society Conference",

publisher = "AAAI Press",

}

TY - GEN

T1 - Forecasting Demand with limited information using gradient tree boosting

AU - Chang, Stephan

AU - Meneguzzi, Felipe

PY - 2017

Y1 - 2017

N2 - Demand forecasting is an important challenge for industries seeking to optimize service quality and expenditures. Generating accurate forecasts is difficult because it depends on the quality of the data available to train predictive models, as well as on the model chosen for the task. We evaluate the approach on two datasets of varying complexity and compare the results with three machine learning algorithms, Results show our approach can outperform these approaches.

AB - Demand forecasting is an important challenge for industries seeking to optimize service quality and expenditures. Generating accurate forecasts is difficult because it depends on the quality of the data available to train predictive models, as well as on the model chosen for the task. We evaluate the approach on two datasets of varying complexity and compare the results with three machine learning algorithms, Results show our approach can outperform these approaches.

UR - http://www.scopus.com/inward/record.url?scp=85029530718&partnerID=8YFLogxK

M3 - Published conference contribution

AN - SCOPUS:85029530718

SP - 227

EP - 230

BT - FLAIRS 2017 - Proceedings of the 30th International Florida Artificial Intelligence Research Society Conference

A2 - Rus, Vasile

A2 - Markov, Zdravko

PB - AAAI Press

T2 - 30th International Florida Artificial Intelligence Research Society Conference, FLAIRS 2017

Y2 - 22 May 2017 through 24 May 2017

ER -

Forecasting Demand with limited information using gradient tree boosting

Abstract

Conference

Bibliographical note

Other files and links

Fingerprint

Cite this