Neural Networks with Transfer Learning and Frequency Decomposition for Wind Speed Prediction with Missing Data

Xiaoou Li () and Yingqin Zhu
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Xiaoou Li: Departamento de Computacion, CINVESTAV-IPN (National Polytechnic Institute), Mexico City 07360, Mexico
Yingqin Zhu: Departamento de Control Automatico, CINVESTAV-IPN (National Polytechnic Institute), Mexico City 07360, Mexico

Mathematics, 2024, vol. 12, issue 8, 1-20

Abstract: This paper presents a novel data-driven approach for enhancing time series forecasting accuracy when faced with missing data. Our proposed method integrates an Echo State Network (ESN) with ARIMA (Autoregressive Integrated Moving Average) modeling, frequency decomposition, and online transfer learning. This combination specifically addresses the challenges missing data introduce in time series prediction. By using the strengths of each technique, our framework offers a robust solution for handling missing data and achieving superior forecasting accuracy in real-world applications. We demonstrate the effectiveness of the proposed model through a wind speed prediction case study. Compared to the existing methods, our approach achieves significant improvement in prediction accuracy, paving the way for more reliable decisionmaking in wind energy operations and management.

Keywords: time series forecasting; neural network; transfer learning; frequency decomposition (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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