Model Monitoring and Dynamic Model Selection in Travel Time-Series Forecasting

Rosa Candela, Pietro Michiardi, Maurizio Filippone, Maria A. Zuluaga*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Accurate travel products price forecasting is a highly desired feature that allows customers to take informed decisions about purchases, and companies to build and offer attractive tour packages. Thanks to machine learning (ML), it is now relatively cheap to develop highly accurate statistical models for price time-series forecasting. However, once models are deployed in production, it is their monitoring, maintenance and improvement which carry most of the costs and difficulties over time. We introduce a data-driven framework to continuously monitor and maintain deployed time-series forecasting models’ performance, to guarantee stable performance of travel products price forecasting models. Under a supervised learning approach, we predict the errors of time-series forecasting models over time, and use this predicted performance measure to achieve both model monitoring and maintenance. We validate the proposed method on a dataset of 18K time-series from flight and hotel prices collected over two years and on two public benchmarks.

Original languageEnglish (US)
Title of host publicationMachine Learning and Knowledge Discovery in Databases
Subtitle of host publicationApplied Data Science Track - European Conference, ECML PKDD 2020, Proceedings
EditorsYuxiao Dong, Dunja Mladenic, Craig Saunders
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages17
ISBN (Print)9783030676667
StatePublished - 2021
EventEuropean Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2020 - Virtual, Online
Duration: Sep 14 2020Sep 18 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12460 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceEuropean Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2020
CityVirtual, Online


  • Forecasting
  • Model maintenance
  • Model monitoring
  • Time-series

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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