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

• URL: http://arxiv.org/abs/2603.19899v1
• Date: Fri, 20 Mar 2026 12:31:08 GMT
• Title: Deep Autocorrelation Modeling for Time-Series Forecasting: Progress and Prospects
• Abstract summary: Autocorrelation is a defining characteristic of time-series data, where each observation is statistically dependent on its predecessors.<n>Recent studies have made strides in tackling these challenges, but a systematic survey examining both aspects remains lacking.<n>This paper provides a comprehensive review of deep time-series forecasting from the perspective of autocorrelation modeling.
• Score: 122.77185406890963
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• FOS: A Large-Scale Temporal Graph Benchmark for Scientific Interdisciplinary Link Prediction [2.9549047539453395]
  Interdisciplinary scientific breakthroughs mostly emerge unexpectedly, and forecasting the formation of novel research fields remains a major challenge.<n>We introduce FOS (Future Of Science), a comprehensive time-aware graph-based benchmark that reconstructs annual co-occurrence graphs of 65,027 research sub-fields.<n>In these graphs, edges denote the co-occurrence of two fields in a single publication and are timestamped with the corresponding publication year.<n>We formulate the prediction of new field-pair linkages as a temporal link-prediction task, emphasizing the "first-time" connections that signify pioneering interdisciplinary directions.
  arXiv  Detail & Related papers  (2025-11-23T22:08:52Z)
• Accuracy Law for the Future of Deep Time Series Forecasting [65.46625911002202]
  Time series forecasting inherently faces a non-zero error lower bound due to its partially observable and uncertain nature.<n>This paper focuses on a fundamental question: how to estimate the performance upper bound of deep time series forecasting.<n>Based on rigorous statistical tests of over 2,800 newly trained deep forecasters, we discover a significant exponential relationship between the minimum forecasting error of deep models and the complexity of window-wise series patterns.
  arXiv  Detail & Related papers  (2025-10-03T05:18:47Z)
• ARIES: Relation Assessment and Model Recommendation for Deep Time Series Forecasting [54.57031153712623]
  ARIES is a framework for assessing relation between time series properties and modeling strategies.<n>We propose the first deep forecasting model recommender, capable of providing interpretable suggestions for real-world time series.
  arXiv  Detail & Related papers  (2025-09-07T13:57:14Z)
• Next-Generation Conflict Forecasting: Unleashing Predictive Patterns through Spatiotemporal Learning [0.0]
  This study presents a neural network architecture for forecasting three distinct types of violence up to 36 months in advance.<n>The model jointly performs probabilistic classification and regression tasks, producing both estimates and expected magnitudes of future events.<n>It is a promising tool for warning systems, humanitarian response planning, and evidence-based peacebuilding initiatives.
  arXiv  Detail & Related papers  (2025-06-08T20:42:29Z)
• Foundation Models for Time Series: A Survey [0.27835153780240135]
  Transformer-based foundation models have emerged as a dominant paradigm in time series analysis.<n>This survey introduces a novel taxonomy to categorize them across several dimensions.
  arXiv  Detail & Related papers  (2025-04-05T01:27:55Z)
• Deep Learning for Time Series Forecasting: A Survey [12.748035569833451]
  We study the previous works and summarize the general paradigms of Deep Time Series Forecasting (DTSF) in terms of model architectures.<n>We take an innovative approach by focusing on the composition of time series and systematically explain important feature extraction methods.
  arXiv  Detail & Related papers  (2025-03-13T09:32:01Z)
• The Clever Hans Mirage: A Comprehensive Survey on Spurious Correlations in Machine Learning [78.13481522957552]
  Machine learning models are sensitive to spurious correlations between non-essential features of the inputs and the corresponding labels.<n>This paper provides a comprehensive survey of this emerging issue, along with a fine-grained taxonomy of existing state-of-the-art methods for addressing spurious correlations in machine learning models.
  arXiv  Detail & Related papers  (2024-02-20T04:49:34Z)
• An End-to-End Time Series Model for Simultaneous Imputation and Forecast [14.756607742477252]
  We develop an end-to-end time series model that aims to learn the inference relation and make a multiple-step ahead forecast. Our framework trains jointly two neural networks, one to learn the feature-wise correlations and the other for the modeling of temporal behaviors.
  arXiv  Detail & Related papers  (2023-06-01T15:08:22Z)
• Temporal Relevance Analysis for Video Action Models [70.39411261685963]
  We first propose a new approach to quantify the temporal relationships between frames captured by CNN-based action models. We then conduct comprehensive experiments and in-depth analysis to provide a better understanding of how temporal modeling is affected.
  arXiv  Detail & Related papers  (2022-04-25T19:06:48Z)
• An Empirical Study: Extensive Deep Temporal Point Process [61.14164208094238]
  We first review recent research emphasis and difficulties in modeling asynchronous event sequences with deep temporal point process.<n>We propose a Granger causality discovery framework for exploiting the relations among multi-types of events.
  arXiv  Detail & Related papers  (2021-10-19T10:15:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.