MP3: Multi-Period Pattern Pre-training forSpatio-Temporal Forecasting

2026-06-12 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Robotics & Autonomous Systems · Depth: Expert, extended

Summary

MP3: Multi-Period Pattern Pre-training is a novel plug-and-play pre-training plugin designed to enhance spatio-temporal forecasting by addressing the "temporal mirage" phenomenon in urban data. It learns multi-period patterns from long time series through multi-period temporal modeling using edge convolution, multi-period spatial modeling with bottleneck projection and a global memory bank, and cross-period pattern interaction via a causality-enhanced Transformer. MP3 seamlessly integrates into existing Spatio-Temporal Graph Neural Network (STGNN) backbones, strengthening their predictive performance. Experiments on five STGNN baselines across five real-world datasets, including the large-scale CA dataset, demonstrate MP3's effectiveness, superior scalability, and strong adaptability, reducing MAE by an average of 4.7% and RMSE by 5.0%.

Key takeaway

For Machine Learning Engineers and AI Scientists working on spatio-temporal forecasting, you should consider integrating pre-training modules like MP3 to overcome the limitations of short-window inputs. This approach, by distilling multi-period patterns from long time series, significantly improves prediction accuracy and model stability, especially for short-horizon forecasts. Implement MP3 as a plug-and-play component to enhance existing STGNN backbones, leveraging its demonstrated scalability on large-scale datasets like CA.

Key insights

MP3 pre-trains multi-period patterns from long time series to resolve "temporal mirage" in spatio-temporal forecasting.

Principles

• Short-window inputs in spatio-temporal data lead to "temporal mirage" due to incomplete period observation.
• Multi-period patterns from long time series are crucial for accurate spatio-temporal prediction.
• Stronger period patterns exert unidirectional causal influence on weaker ones.

Method

MP3 employs context-aware embedding, FFT-based multi-period identification, and 2D reshaping. It then uses edge convolution for temporal modeling, bottleneck projection with a global memory bank for spatial modeling, and a causality-enhanced Transformer for cross-period interaction.

In practice

• Integrate MP3 as a frozen pre-training plugin into existing STGNN backbones.
• Utilize a gating mechanism to adaptively fuse pre-trained multi-period features with short-term inputs.
• Apply Fast Fourier Transform (FFT) to identify dominant period patterns for time series reconstruction.

Topics

• Spatio-Temporal Forecasting
• Pre-training
• Graph Neural Networks
• Multi-Period Patterns
• Temporal Mirage
• Traffic Prediction

Code references

• YAN-outlook/MP3

Best for: AI Scientist, Machine Learning Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.