  Transformers have gained popularity in time series forecasting for their
ability to capture long-sequence interactions. However, their high memory and
computing requirements pose a critical bottleneck for long-term forecasting. To
address this, we propose TSMixer, a lightweight neural architecture exclusively
composed of multi-layer perceptron (MLP) modules for multivariate forecasting
and representation learning on patched time series. Inspired by MLP-Mixer's
success in computer vision, we adapt it for time series, addressing challenges
and introducing validated components for enhanced accuracy. This includes a
novel design paradigm of attaching online reconciliation heads to the MLP-Mixer
backbone, for explicitly modeling the time-series properties such as hierarchy
and channel-correlations. We also propose a novel Hybrid channel modeling and
infusion of a simple gating approach to effectively handle noisy channel
interactions and generalization across diverse datasets. By incorporating these
lightweight components, we significantly enhance the learning capability of
simple MLP structures, outperforming complex Transformer models with minimal
computing usage. Moreover, TSMixer's modular design enables compatibility with
both supervised and masked self-supervised learning methods, making it a
promising building block for time-series Foundation Models. TSMixer outperforms
state-of-the-art MLP and Transformer models in forecasting by a considerable
margin of 8-60%. It also outperforms the latest strong benchmarks of
Patch-Transformer models (by 1-2%) with a significant reduction in memory and
runtime (2-3X). The source code of our model is officially released as
PatchTSMixer in the HuggingFace. Model:
https://huggingface.co/docs/transformers/main/en/model_doc/patchtsmixer
Examples: https://github.com/ibm/tsfm/#notebooks-links
