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.
|Title of host publication||FLAIRS 2017 - Proceedings of the 30th International Florida Artificial Intelligence Research Society Conference|
|Editors||Vasile Rus, Zdravko Markov|
|Number of pages||4|
|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||30th International Florida Artificial Intelligence Research Society Conference, FLAIRS 2017|
|Period||22/05/17 → 24/05/17|
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Copyright © 2017, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.