Self-boosted Time-series Forecasting with Multi-task and Multi-view Learning
This addresses forecasting challenges in domains like finance and energy where external data access is limited, offering an incremental improvement by leveraging internal decomposition.
The paper tackles the problem of time-series forecasting without requiring additional external features by proposing a self-boosted mechanism that decomposes the original series into multiple series for multi-task and multi-view learning, achieving superior performance over state-of-the-art baselines on three real-world datasets.
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.