Multimodal Forecasting for Commodity Prices Using Spectrogram-Based and Time Series Representations

📰 ArXiv cs.AI

Spectrogram-Enhanced Multimodal Fusion (SEMF) combines spectral and temporal representations for more accurate commodity price forecasting

advanced Published 31 Mar 2026
Action Steps
  1. Transform target time series into Morlet wavelet spectrograms
  2. Extract localized features using a Vision Transformer encoder
  3. Combine spectral and temporal representations for forecasting
  4. Evaluate the performance of SEMF against baseline models
Who Needs to Know This

Data scientists and machine learning engineers on a team can benefit from this approach to improve forecasting accuracy, while product managers can utilize these insights to inform business decisions

Key Insight

💡 Multimodal fusion of spectral and temporal representations can improve forecasting accuracy in complex time series data

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💡 Combine spectral & temporal representations for more accurate commodity price forecasting with SEMF

Key Takeaways

Spectrogram-Enhanced Multimodal Fusion (SEMF) combines spectral and temporal representations for more accurate commodity price forecasting

Full Article

Title: Multimodal Forecasting for Commodity Prices Using Spectrogram-Based and Time Series Representations

Abstract:
arXiv:2603.27321v1 Announce Type: cross Abstract: Forecasting multivariate time series remains challenging due to complex cross-variable dependencies and the presence of heterogeneous external influences. This paper presents Spectrogram-Enhanced Multimodal Fusion (SEMF), which combines spectral and temporal representations for more accurate and robust forecasting. The target time series is transformed into Morlet wavelet spectrograms, from which a Vision Transformer encoder extracts localized, f
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