  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.
