  The explosion of time series data in recent years has brought a flourish of
new time series analysis methods, for forecasting, clustering, classification
and other tasks. The evaluation of these new methods requires either collecting
or simulating a diverse set of time series benchmarking data to enable reliable
comparisons against alternative approaches. We propose GeneRAting TIme Series
with diverse and controllable characteristics, named GRATIS, with the use of
mixture autoregressive (MAR) models. We simulate sets of time series using MAR
models and investigate the diversity and coverage of the generated time series
in a time series feature space. By tuning the parameters of the MAR models,
GRATIS is also able to efficiently generate new time series with controllable
features. In general, as a costless surrogate to the traditional data
collection approach, GRATIS can be used as an evaluation tool for tasks such as
time series forecasting and classification. We illustrate the usefulness of our
time series generation process through a time series forecasting application.
