A Multimodal Time-Series Forecasting Framework Integrating Wavelet Transform and Semantic Embedding for Intelligent Monitoring Systems
TL;DR
A multimodal long-term forecasting framework that integrates frequency-aware signal decomposition with semantic-enhanced representation learning and provides a modular and interpretable architecture combining frequency-aware and semantic-aware processing for intelligent system management is proposed.
Abstract
Intelligent monitoring systems in domains such as renewable energy, electrical grid management, and environmental sensing continuously generate high-dimensional multivariate time-series data characterized by non-stationarity and multi-scale temporal dependencies. Accurate long-term forecasting of system parameters is essential for proactive maintenance, operational scheduling, and cost reduction, yet many existing models rely solely on numerical sequences and lack mechanisms to incorporate higher-level contextual information. This study proposes a multimodal long-term forecasting framework that integrates frequency-aware signal decomposition with semantic-enhanced representation learning. The framework comprises four components: (1) a wavelet-based feature extraction module that captures multi-scale periodic patterns through energy-guided frequency selection; (2) a semantic feature extraction module that encodes statistical summaries of the input into language embeddings via a pretrained language model; (3) a cross-attention fusion module that dynamically aligns temporal and semantic representations; and (4) a multi-scale MLP ensemble for robust prediction. Experiments on three benchmark datasets—Solar Power, ETTh1, and Weather—show that the framework achieves competitive accuracy against strong baselines, including PatchTST and iTransformer, with its strongest results on data exhibiting complex multi-scale seasonal structure, where it attains the second-best mean squared error on the Weather dataset. A controlled ablation isolating the pretrained embedding from a direct numerical encoding of the same statistics indicates a small, dataset-specific benefit that is comparable in magnitude to seed-to-seed variation. Overall, the proposed framework provides a modular and interpretable architecture combining frequency-aware and semantic-aware processing for intelligent system management.