ONE-NAS: An Online NeuroEvolution based Neural Architecture Search for Time Series Forecasting
This addresses the need for adaptive forecasting in domains like finance and energy, though it appears incremental as it builds on existing neuroevolution and NAS techniques for a specific application.
The paper tackles the problem of time series forecasting models struggling with unpredictable patterns over long time scales by introducing ONE-NAS, an online neural architecture search algorithm that automatically designs and trains recurrent neural networks without pretraining, outperforming traditional statistical methods and online ARIMA strategies on real-world datasets like wind turbine and DJIA data.
Time series forecasting (TSF) is one of the most important tasks in data science, as accurate time series (TS) predictions can drive and advance a wide variety of domains including finance, transportation, health care, and power systems. However, real-world utilization of machine learning (ML) models for TSF suffers due to pretrained models being able to learn and adapt to unpredictable patterns as previously unseen data arrives over longer time scales. To address this, models must be periodically retained or redesigned, which takes significant human and computational resources. This work presents the Online NeuroEvolution based Neural Architecture Search (ONE-NAS) algorithm, which to the authors' knowledge is the first neural architecture search algorithm capable of automatically designing and training new recurrent neural networks (RNNs) in an online setting. Without any pretraining, ONE-NAS utilizes populations of RNNs which 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 a univariate Dow Jones Industrial Average (DJIA) dataset, and is shown to outperform traditional statistical time series forecasting, including naive, moving average, and exponential smoothing methods, as well as state of the art online ARIMA strategies.