Time series forecasting (TSF) is one of the most important tasks in data science given the fact that accurate time series (TS) predictive models play a major role across a wide variety of domains including finance, transportation, health care, and power systems. Real-world utilization of machine learning (ML) typically involves (pre-)training models on collected, historical data and then applying them to unseen data points. However, in real-world applications, time series data streams are usually non-stationary and trained ML models usually, over time, face the problem of data or concept drift. To address this issue, models must be periodically retrained or redesigned, which takes significant human and computational resources. Additionally, historical data may not even exist to re-train or re-design model with. As a result, it is highly desirable that models are designed and trained in an online fashion. This work presents the Online NeuroEvolution-based Neural Architecture Search (ONE-NAS) algorithm, which is a novel neural architecture search method capable of automatically designing and dynamically training recurrent neural networks (RNNs) for online forecasting tasks. Without any pre-training, ONE-NAS utilizes populations of RNNs that are continuously updated with new network structures and weights in response to new multivariate input data. ONE-NAS is tested on real-world, large-scale multivariate wind turbine data as well as the univariate Dow Jones Industrial Average (DJIA) dataset. Results demonstrate that ONE-NAS outperforms traditional statistical time series forecasting methods, including online linear regression, fixed long short-term memory (LSTM) and gated recurrent unit (GRU) models trained online, as well as state-of-the-art, online ARIMA strategies.
翻译:时间序列预测(TSF)是数据科学中最重要的任务之一,因为准确的时间序列(TS)预测模型在金融、运输、医疗保健和电力系统等广泛领域发挥着主要作用。 机器学习(ML)的现实世界利用通常包括(预)关于所收集的历史数据的培训模型,然后将其应用于无形的数据点。然而,在现实应用中,时间序列数据流通常是非静止和经过培训的 ML模型,通常面临数据或概念流流的问题。要解决这个问题,模型必须定期重新培训或重新设计,这需要大量的人力和计算资源。此外,历史数据甚至可能甚至无法用于再培训或重新设计模型。因此,非常可取的是,模型的设计和培训模式要以在线方式应用(基于在线的神经革命建筑搜索(One-NAS)算法,这是一个新的神经结构搜索法,能够自动设计和动态地培训经常性的神经网络(RNNIS),用于在线预测任务。在任何前阶段内,包括不断测试的IMIS数据结构中,将数据作为不断测试的大规模数据结构。