  A robust model for time series forecasting is highly important in many
domains, including but not limited to financial forecast, air temperature and
electricity consumption. To improve forecasting performance, traditional
approaches usually require additional feature sets. However, adding more
feature sets from different sources of data is not always feasible due to its
accessibility limitation. In this paper, we propose a novel self-boosted
mechanism in which the original time series is decomposed into multiple time
series. These time series played the role of additional features in which the
closely related time series group is used to feed into multi-task learning
model, and the loosely related group is fed into multi-view learning part to
utilize its complementary information. We use three real-world datasets to
validate our model and show the superiority of our proposed method over
existing state-of-the-art baseline methods.
