  Recent work has shown the efficiency of deep learning models such as Fully
Convolutional Networks (FCN) or Recurrent Neural Networks (RNN) to deal with
Time Series Regression (TSR) problems. These models sometimes need a lot of
data to be able to generalize, yet the time series are sometimes not long
enough to be able to learn patterns. Therefore, it is important to make use of
information across time series to improve learning. In this paper, we will
explore the idea of using meta-learning for quickly adapting model parameters
to new short-history time series by modifying the original idea of Model
Agnostic Meta-Learning (MAML) \cite{finn2017model}. Moreover, based on prior
work on multimodal MAML \cite{vuorio2019multimodal}, we propose a method for
conditioning parameters of the model through an auxiliary network that encodes
global information of the time series to extract meta-features. Finally, we
apply the data to time series of different domains, such as pollution
measurements, heart-rate sensors, and electrical battery data. We show
empirically that our proposed meta-learning method learns TSR with few data
fast and outperforms the baselines in 9 of 12 experiments.
