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

TL;DR

Amplifier introduces an energy amplification technique to improve low-energy component modeling in time series forecasting, integrating frequency domain analysis and channel interaction for enhanced accuracy and efficiency.

Contribution

The paper presents a novel energy amplification and restoration method combined with frequency domain modeling and channel interaction blocks for improved time series forecasting.

Findings

Outperforms state-of-the-art methods on eight benchmarks

Effectively captures low-energy components in time series data

Enhances forecasting accuracy and efficiency

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.