  Meta-forecasting is a newly emerging field which combines meta-learning and
time series forecasting. The goal of meta-forecasting is to train over a
collection of source time series and generalize to new time series
one-at-a-time. Previous approaches in meta-forecasting achieve competitive
performance, but with the restriction of training a separate model for each
sampling frequency. In this work, we investigate meta-forecasting over
different sampling frequencies, and introduce a new model, the Continuous
Frequency Adapter (CFA), specifically designed to learn frequency-invariant
representations. We find that CFA greatly improves performance when
generalizing to unseen frequencies, providing a first step towards forecasting
over larger multi-frequency datasets.

Machine Learning