Self-boosted Time-series Forecasting with Multi-task and Multi-view Learning

TL;DR

This paper introduces a self-boosted multi-task and multi-view learning model for time-series forecasting that decomposes original data into multiple series to enhance prediction accuracy without requiring additional external features.

Contribution

The paper proposes a novel self-boosted mechanism that decomposes time series into multiple series for multi-task and multi-view learning, improving forecasting performance without extra feature sets.

Findings

Outperforms existing state-of-the-art methods on real-world datasets

Demonstrates the effectiveness of decomposition-based multi-task learning

Shows the benefit of multi-view learning with loosely related series

Abstract

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.