Affiliation of Author(s):计算机科学与技术学院/人工智能学院/软件学院
Journal:APPLIED INTELLIGENCE
Key Words:Time series forecasting (TSF) Incremental learning (IL) Extreme learning machine (ELM) Incremental extreme learning machine (IELM) Dual weights optimization incremental learning (w(2)IL) algorithm
Abstract:In this paper, a novel dual weights optimization Incremental Learning (w(2)IL) algorithm is developed to solve Time Series Forecasting (TSF) problem. The w(2)IL algorithm utilizes IELM as the base learner, while its incremental learning scheme is implemented by employing a newly designed Adaptively Weighted Predictors Aggregation (AdaWPA) subalgorithm to aggregate the existing base predictors with the ones generated upon the new data. There exist two major innovations within w(2)IL, namely, the well-designed Adaptive Samples Weights Initialization (AdaSWI) and AdaWPA subalgorithms. The AdaSWI subalgorithm initializes the samples' weights adaptively based on the generated base models' prediction errors, and fine-tunes the samples' weights based on the distances from the samples to the clustering centers of base models' training datasets, achieving more appropriate samples weights initialization. While the AdaWPA algorithm adaptively adjusts base predictors' weights based on prediction instances and integrates the base predictors employing these adjusted weights. Besides, the AdaWPA subalgorithm makes use of Fuzzy C-Means (FCM) clustering algorithm for distance measurement, further reducing computational complexity and storage space of the algorithm. The w(2)IL algorithm constructed in this way possesses significantly superior prediction performance compared with other existing good algorithms, which has been verified through experimental results on six benchmark real-world TSF datasets.
ISSN No.:0924-669X
Translation or Not:no
Date of Publication:2019-10-01
Co-author:李锦华
Correspondence Author:dq
Date of Publication:2019-10-01