TSNN: A Non-parametric and Interpretable Framework for Traffic Time Series Forecasting

2026-05-09

AI Summary

This study addresses the challenges of high model complexity and poor interpretability in traffic time series forecasting by proposing TSNN, a novel framework that introduces non-parametric methods to this domain for the first time. TSNN employs a parameter-free multi-layer architecture to decouple temporal patterns, explicitly models periodicity, and constructs a memory bank from the training set to enable prediction through similarity matching. Requiring no trainable parameters, TSNN achieves both high interpretability and competitive predictive performance. Experimental results on four real-world traffic flow datasets demonstrate that TSNN attains forecasting accuracy comparable to state-of-the-art deep learning models, while its decision logic is validated through intuitive visualizations.

Abstract

Although many complex models were proposed to analyze time series data, some studies have demonstrated remarkable performance with simpler structures. A recent study proposed a non-parametric framework for 3D point cloud classification, which has the potential to be adapted for time series forecasting and enable interpretability. Inspired by the previous works, we present TSNN, a non-parametric and interpretable framework for traffic time series forecasting. TSNN consists of multiple layers that decouple the time series by matching the entries in a memory bank, where the memory bank is constructed using a similar matching process within the training set. It leverages the periodicity in traffic data to enhance forecasting accuracy while maintaining a simple model architecture. The proposed model operates without trainable parameters, preserving its inherent interpretability. In the experiments, TSNN achieves competitive performance compared to the typical deep learning models in four real-world traffic flow datasets. We also visualize the decoupling process to show the effectiveness of the components. Finally, we demonstrate the interpretability of the model and illustrate the contribution of each time step within the memory bank.

Problem

• traffic time series forecasting
• non-parametric
• interpretability
• time series prediction
• model interpretability

Innovation

• non-parametric
• interpretable
• memory bank
• time series forecasting
• traffic flow