Open Access

ARTICLE

Automated Machine Learning Algorithm Using Recurrent Neural Network to Perform Long-Term Time Series Forecasting

Ying Su¹, Morgan C. Wang¹, Shuai Liu^2,*

1 Department of Statistics and Data Science, University of Central Florida, Orlando, FL, 32816-2370, USA
2 School of Mathematics and Statistics, Beijing Technology and Business University, Beijing, 100048, China

* Corresponding Author: Shuai Liu. Email:

Computers, Materials & Continua 2024, 78(3), 3529-3549. https://doi.org/10.32604/cmc.2024.047189

Received 28 October 2023; Accepted 16 January 2024; Issue published 26 March 2024

Abstract

Long-term time series forecasting stands as a crucial research domain within the realm of automated machine learning (AutoML). At present, forecasting, whether rooted in machine learning or statistical learning, typically relies on expert input and necessitates substantial manual involvement. This manual effort spans model development, feature engineering, hyper-parameter tuning, and the intricate construction of time series models. The complexity of these tasks renders complete automation unfeasible, as they inherently demand human intervention at multiple junctures. To surmount these challenges, this article proposes leveraging Long Short-Term Memory, which is the variant of Recurrent Neural Networks, harnessing memory cells and gating mechanisms to facilitate long-term time series prediction. However, forecasting accuracy by particular neural network and traditional models can degrade significantly, when addressing long-term time-series tasks. Therefore, our research demonstrates that this innovative approach outperforms the traditional Autoregressive Integrated Moving Average (ARIMA) method in forecasting long-term univariate time series. ARIMA is a high-quality and competitive model in time series prediction, and yet it requires significant preprocessing efforts. Using multiple accuracy metrics, we have evaluated both ARIMA and proposed method on the simulated time-series data and real data in both short and long term. Furthermore, our findings indicate its superiority over alternative network architectures, including Fully Connected Neural Networks, Convolutional Neural Networks, and Nonpooling Convolutional Neural Networks. Our AutoML approach enables non-professional to attain highly accurate and effective time series forecasting, and can be widely applied to various domains, particularly in business and finance.

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Keywords

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