Forecasting Early with Meta Learning
Shayan Jawed Kiran Madhusudhanan Vijaya Krishna Yalavarthi Lars Schmidt-Thieme
In the early observation period of a time series, there might be only a few
historic observations available to learn a model. However, in cases where an
existing prior set of datasets is available, Meta learning methods can be
applicable. In this paper, we devise a Meta learning method that exploits
samples from additional datasets and learns to augment time series through
adversarial learning as an auxiliary task for the target dataset. Our model
(FEML), is equipped with a shared Convolutional backbone that learns features
for varying length inputs from different datasets and has dataset specific
heads to forecast for different output lengths. We show that FEML can meta
learn across datasets and by additionally learning on adversarial generated
samples as auxiliary samples for the target dataset, it can improve the
forecasting performance compared to single task learning, and various solutions
adapted from Joint learning, Multi-task learning and classic forecasting
baselines.