Deep Autocorrelation Modeling for Time-Series Forecasting: Progress and Prospects

TL;DR

This paper systematically reviews deep time-series forecasting methods focusing on autocorrelation modeling, proposing a new taxonomy and analyzing the evolution of architectures and learning objectives from an autocorrelation perspective.

Contribution

It introduces a novel taxonomy for recent deep forecasting literature and provides a comprehensive, autocorrelation-centric analysis of the field's progression.

Findings

Proposes a new taxonomy encompassing model architectures and learning objectives.

Provides a unified analysis of the evolution of deep time-series forecasting.

Highlights the importance of autocorrelation modeling in forecasting performance.

Abstract

Autocorrelation is a defining characteristic of time-series data, where each observation is statistically dependent on its predecessors. In the context of deep time-series forecasting, autocorrelation arises in both the input history and the label sequences, presenting two central research challenges: (1) designing neural architectures that model autocorrelation in history sequences, and (2) devising learning objectives that model autocorrelation in label sequences. Recent studies have made strides in tackling these challenges, but a systematic survey examining both aspects remains lacking. To bridge this gap, this paper provides a comprehensive review of deep time-series forecasting from the perspective of autocorrelation modeling. In contrast to existing surveys, this work makes two distinctive contributions. First, it proposes a novel taxonomy that encompasses recent literature on both model architectures and learning objectives -- whereas prior surveys neglect or inadequately discuss the latter aspect. Second, it offers a thorough analysis of the motivations, insights, and progression of the surveyed literature from a unified, autocorrelation-centric perspective, providing a holistic overview of the evolution of deep time-series forecasting. The full list of papers and resources is available at https://github.com/Master-PLC/Awesome-TSF-Papers.