MixLinear: 极低资源多变量时间序列预测的0.1K参数模型
MixLinear: Extreme Low Resource Multivariate Time Series Forecasting with 0.1K Parameters
AI总结：
MixLinear是一种针对资源受限设备设计的超轻量级多变量时间序列预测模型，通过减少参数规模至0.1K实现高效计算，同时保持预测性能。
摘要：
近年来，长期时间序列预测（LTSF）受到了越来越多的关注，其目的是通过分析大量历史时间序列数据来识别模式和趋势，以预测长期的未来值。由于其复杂的时序依赖性和高计算需求，LTSF存在显著的挑战。尽管基于Transformer的模型提供了高预测准确性，但它们通常过于计算密集，无法在硬件受限的设备上部署。另一方面，线性模型通过在时域中使用分解方法或在频域中使用紧凑的表示来减少计算开销。在本文中，我们提出了一种名为MixLinear的超轻量级多变量时间序列预测模型，专门设计用于资源受限的设备。MixLinear通过在时域中建模段内和段间变化，并在频域中从低维潜在空间中提取频域变化，有效捕捉了时域和频域的特征。通过将下采样n长度输入/输出单层线性模型的参数规模从O(n²)减少到O(n)，MixLinear实现了高效的计算而不牺牲准确性。对四个基准数据集的广泛评估表明，MixLinear在参数数量显著更少（0.1K）的情况下，取得了与或超越最先进模型相媲美的预测性能，这使其非常适合在计算能力有限的设备上部署。
英文摘要：
Recently, there has been a growing interest in Long-term Time Series Forecasting (LTSF), which involves predicting long-term future values by analyzing a large amount of historical time-series data to identify patterns and trends. There exist significant challenges in LTSF due to its complex temporal dependencies and high computational demands. Although Transformer-based models offer high forecasting accuracy, they are often too compute-intensive to be deployed on devices with hardware constraints. On the other hand, the linear models aim to reduce the computational overhead by employing either decomposition methods in the time domain or compact representations in the frequency domain. In this paper, we propose MixLinear, an ultra-lightweight multivariate time series forecasting model specifically designed for resource-constrained devices. MixLinear effectively captures both temporal and frequency domain features by modeling intra-segment and inter-segment variations in the time domain and extracting frequency variations from a low-dimensional latent space in the frequency domain. By reducing the parameter scale of a downsampled $n$-length input/output one-layer linear model from $O(n^2)$ to $O(n)$, MixLinear achieves efficient computation without sacrificing accuracy. Extensive evaluations with four benchmark datasets show that MixLinear attains forecasting performance comparable to, or surpassing, state-of-the-art models with significantly fewer parameters ($0.1K$), which makes it well-suited for deployment on devices with limited computational capacity.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
