多步超前时间序列预测的PSO-MISMO建模策略
PSO-MISMO Modeling Strategy for Multi-Step-Ahead Time Series Prediction
摘要：
多步时间序列预测是时间序列建模与预测领域中最具挑战性的研究课题之一，一直处于研究之中。最近，多输入多输出（MISMO）建模策略已被提出作为一个有前途的替代多步提前时间序列预测，表现出的优势相比，目前的两个主要策略，迭代和直接的策略。建立在已建立的MISMO策略，本研究提出了一种基于粒子群优化（PSO）的MISMO建模策略，这是能够确定的子模型的数量在一个自适应的模式，具有不同的预测视野。而不是从已建立的MISMO中获得具有相等大小的预测范围的清晰划分，所提出的PSO-MISMO策略，用神经网络实现，采用启发式方法来创建具有不同大小的预测范围的灵活划分，并生成相应的子模型，在模型构建中提供了相当大的灵活性，这已经用模拟和真实数据集进行了验证。
英文摘要：
Multi-step-ahead time series prediction is one of the most challenging research topics in the field of time series modeling and prediction, and is continually under research. Recently, the multiple-input several multiple-outputs (MISMO) modeling strategy has been proposed as a promising alternative for multi-step-ahead time series prediction, exhibiting advantages compared with the two currently dominating strategies, the iterated and the direct strategies. Built on the established MISMO strategy, this study proposes a particle swarm optimization (PSO)-based MISMO modeling strategy, which is capable of determining the number of sub-models in a self-adaptive mode, with varying prediction horizons. Rather than deriving crisp divides with equal-size s prediction horizons from the established MISMO, the proposed PSO-MISMO strategy, implemented with neural networks, employs a heuristic to create flexible divides with varying sizes of prediction horizons and to generate corresponding sub-models, providing considerable flexibility in model construction, which has been validated with simulated and real datasets.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
