Ensemble learning‐based modeling and short‐term forecasting algorithm for time series with small sample

作者：Yang Zhang, Gang Ren, Xiaojie Liu, Guanglan Gao, Mingdong Zhu · 发表于：Engineering Reports · 年份：2021 · DOI：10.1002/eng2.12486 · 被引用次数：15 · 研究领域：Energy Load and Power Forecasting、Traffic Prediction and Management Techniques、Time Series Analysis and Forecasting

Abstract Due to missing data or a short sensing period, only a minimal amount of data can be captured in some IoT cases. Making accurate time series predictions with tiny samples is a huge task. To address this problem, this article proposes a time series modeling and prediction method based on gradient boosting. Our ensemble model is obtained by iteratively combining a set of simple learners. Each iteration inputs only part of the external attributes, thus solving the dimensional disaster problem. Simple learners are selected based on the attributes of the current input and can benefit from the researcher's expertise. In short‐term forecasting of electricity consumption, our method's average and maximum relative errors are 2.72% and 5.30%, respectively, which is much better than the reference approach.