A Temporal Linear Network for Time Series Forecasting

TL;DR

This paper introduces the Temporal Linear Net (TLN), a linear architecture for time series forecasting that captures temporal dependencies, offering interpretability and efficiency, and demonstrating competitive performance against complex models.

Contribution

The paper presents TLN, a novel linear model that extends linear models with temporal and feature-wise dependency capturing, maintaining interpretability and computational efficiency.

Findings

TLN effectively captures temporal dependencies in multivariate data.

TLN maintains interpretability through its linear structure.

TLN competes with complex models in forecasting accuracy.

Abstract

Recent research has challenged the necessity of complex deep learning architectures for time series forecasting, demonstrating that simple linear models can often outperform sophisticated approaches. Building upon this insight, we introduce a novel architecture the Temporal Linear Net (TLN), that extends the capabilities of linear models while maintaining interpretability and computational efficiency. TLN is designed to effectively capture both temporal and feature-wise dependencies in multivariate time series data. Our approach is a variant of TSMixer that maintains strict linearity throughout its architecture. TSMixer removes activation functions, introduces specialized kernel initializations, and incorporates dilated convolutions to handle various time scales, while preserving the linear nature of the model. Unlike transformer-based models that may lose temporal information due to their permutation-invariant nature, TLN explicitly preserves and leverages the temporal structure of the input data. A key innovation of TLN is its ability to compute an equivalent linear model, offering a level of interpretability not found in more complex architectures such as TSMixer. This feature allows for seamless conversion between the full TLN model and its linear equivalent, facilitating both training flexibility and inference optimization.