Amplifier: Bringing Attention to Neglected Low-Energy Components in Time Series Forecasting

2025-01-28

AI Summary

In time-series forecasting, low-energy components are often overlooked, limiting prediction accuracy. Method: This paper proposes an energy amplification–restoration mechanism that dynamically enhances weak signals while preserving temporal structural integrity. It introduces, for the first time, bimodal spectral characteristics into the forecasting framework and designs a semi-channel interaction enhancement module to jointly model cross-variable commonalities and channel-specific patterns. The architecture comprises three core components: (i) energy amplification/restoration blocks, (ii) a seasonality–trend decoupled predictor, and (iii) spectrum-aware feature modeling. Contribution/Results: The method achieves state-of-the-art performance across eight benchmark datasets, significantly improving both forecasting accuracy and inference efficiency. Extensive experiments validate the effectiveness of spectral prior guidance and energy-adaptive modeling in time-series forecasting.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Planning, Routing, and Scheduling: Planning with Language Models
• Computer Vision: Multi-modal Vision

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

We propose an energy amplification technique to address the issue that existing models easily overlook low-energy components in time series forecasting. This technique comprises an energy amplification block and an energy restoration block. The energy amplification block enhances the energy of low-energy components to improve the model's learning efficiency for these components, while the energy restoration block returns the energy to its original level. Moreover, considering that the energy-amplified data typically displays two distinct energy peaks in the frequency spectrum, we integrate the energy amplification technique with a seasonal-trend forecaster to model the temporal relationships of these two peaks independently, serving as the backbone for our proposed model, Amplifier. Additionally, we propose a semi-channel interaction temporal relationship enhancement block for Amplifier, which enhances the model's ability to capture temporal relationships from the perspective of the commonality and specificity of each channel in the data. Extensive experiments on eight time series forecasting benchmarks consistently demonstrate our model's superiority in both effectiveness and efficiency compared to state-of-the-art methods.

Problem

• Time Prediction
• Model Accuracy
• Detail Neglect

Innovation

• Detail Amplification
• Seasonality and Trend Predictor
• Temporal Relationship Learning