Patch-Level Tokenization with CNN Encoders and Attention for Improved Transformer Time-Series Forecasting

2026-01-18

AI Summary

This work addresses the limitations of Transformer-based models in long-sequence multivariate time series forecasting, particularly their inadequate input representation quality and structural modeling capacity. To overcome these challenges, the authors propose a two-stage framework: first, a convolutional neural network (CNN) extracts local dynamic features from fixed-length temporal segments and generates compact patch-level token embeddings; subsequently, a Transformer encoder with attention mechanisms models the global dependencies among these segments. By decoupling local feature extraction from global dependency modeling, the approach enhances both scalability and representational power. Experimental results on synthetic multivariate time series datasets demonstrate that the proposed method significantly outperforms CNN baselines under long input sequences and achieves performance comparable to state-of-the-art patch-based Transformer models.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Computer Vision: Multi-modal Vision
• Natural Language Processing: (Large) Language Models

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

Transformer-based models have shown strong performance in time-series forecasting by leveraging self-attention to model long-range temporal dependencies. However, their effectiveness depends critically on the quality and structure of input representations derived from raw multivariate time-series data, particularly as sequence length and data scale increase. This paper proposes a two-stage forecasting framework that explicitly separates local temporal representation learning from global dependency modelling. In the proposed approach, a convolutional neural network operates on fixed-length temporal patches to extract short-range temporal dynamics and non-linear feature interactions, producing compact patch-level token embeddings. Token-level self-attention is applied during representation learning to refine these embeddings, after which a Transformer encoder models inter-patch temporal dependencies to generate forecasts. The method is evaluated on a synthetic multivariate time-series dataset with controlled static and dynamic factors, using an extended sequence length and a larger number of samples. Experimental results demonstrate that the proposed framework consistently outperforms a convolutional baseline under increased temporal context and remains competitive with a strong patch-based Transformer model. These findings indicate that structured patch-level tokenization provides a scalable and effective representation for multivariate time-series forecasting, particularly when longer input sequences are considered.

Problem

• time-series forecasting
• input representation
• multivariate time-series
• sequence length
• temporal dependencies

Innovation

• patch-level tokenization
• CNN encoder
• self-attention
• time-series forecasting
• two-stage framework