  Time series forecasting is a key component in many industrial and business
decision processes and recurrent neural network (RNN) based models have
achieved impressive progress on various time series forecasting tasks. However,
most of the existing methods focus on single-task forecasting problems by
learning separately based on limited supervised objectives, which often suffer
from insufficient training instances. As the Transformer architecture and other
attention-based models have demonstrated its great capability of capturing long
term dependency, we propose two self-attention based sharing schemes for
multi-task time series forecasting which can train jointly across multiple
tasks. We augment a sequence of paralleled Transformer encoders with an
external public multi-head attention function, which is updated by all data of
all tasks. Experiments on a number of real-world multi-task time series
forecasting tasks show that our proposed architectures can not only outperform
the state-of-the-art single-task forecasting baselines but also outperform the
RNN-based multi-task forecasting method.
