Title: Deep Heterogeneity: A New Paradigm for  Time Series Forecasting
Primary area: learning on time series and dynamical systems
Keywords: Time series forecasting, Change modeling, Adaptive fusion, Multi-resolution analysis, Deep learning

Abstract: Time series forecasting frequently employs signal decomposition to disentangle complex series into simpler, more structured components. However, a prevalent limitation within this paradigm is the principle of Shallow Heterogeneity, where structurally homogeneous processing blocks are indiscriminately applied to signal components with fundamentally different properties. This misalignment between a model's fixed inductive biases and the heterogeneous nature of the decomposed signals imposes a significant bottleneck on forecasting accuracy. To address this challenge, we propose a novel framework centered on the principle of Deep Heterogeneity, instantiated in our model, HeteroMixer . Our model first decomposes the series using a wavelet transform and then introduces a deeply heterogeneous architecture, featuring a powerful, dual-domain Trend-Seasonality Extractor (TSE) specifically for the vital low-frequency trend, alongside lightweight expert modules for high-frequency details. Furthermore, we replace static signal reconstruction with a novel Hybrid Prediction Framework, where an attention-based Multi-scale Fusion Transformer (MFT) learns to adaptively predict a corrective residual to a stable, classical baseline. Extensive experiments on seven challenging benchmarks demonstrate that HeteroMixer  establishes a new state-of-the-art, significantly outperforming existing methods. Our work validates the thesis that deeply aligning specialized, heterogeneous architectural biases with the intrinsic properties of signal components is a crucial strategy for advancing forecasting accuracy.