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

TL;DR

This paper introduces a two-stage time-series forecasting framework that combines CNN-based patch tokenization with Transformer models, improving scalability and accuracy for long sequences.

Contribution

The paper proposes a novel patch-level tokenization method with CNN encoders and attention, explicitly separating local and global modeling for better long-range dependency capture.

Findings

Outperforms CNN baseline with longer sequences

Remains competitive with strong patch-based Transformer models

Provides scalable, effective representations for multivariate time-series

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.