ONE-NAS: An Online NeuroEvolution based Neural Architecture Search for Time Series Forecasting

• URL: http://arxiv.org/abs/2202.13471v1
• Date: Sun, 27 Feb 2022 22:58:32 GMT
• Title: ONE-NAS: An Online NeuroEvolution based Neural Architecture Search for Time Series Forecasting
• Authors: Zimeng Lyu, Travis Desell
• Abstract summary: This work presents the Online NeuroEvolution based Neural Architecture Search (ONE-NAS) algorithm. ONE-NAS is the first neural architecture search algorithm capable of automatically designing and training new recurrent neural networks (RNNs) in an online setting. It is shown to outperform traditional statistical time series forecasting, including naive, moving average, and exponential smoothing methods.
• Score: 3.3758186776249928
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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