A multi-modal approach for mixed-frequency time series forecasting
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This study proposes a novel multimodal approach for mixed-frequency time series forecasting in the oil industry, enabling the use of high-frequency (HF) data in their original frequency. We specifically address the challenge of integrating HF data streams, such as pressure and temperature measurements, with daily time series without introducing noise. Our approach was compared with existing econometric regression model mixed-data sampling (MIDAS) and with the data-driven models N-HiTS and a GRU-based network, across short-, medium-, and long-term prediction horizons. Additionally, we validated the proposed method on datasets from other domains beyond the oil industry. The experimental results indicate that our multimodal approach significantly improves long-term prediction accuracy.
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Forecasting, Mixed-frequency time series, Multimodal learning, Pre-salt oil field
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Inglês
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Neural Computing and Applications, v. 36, n. 34, p. 21581-21605, 2024.
