mlsanityData to 14 Oct 2026

Deep Learning for Time Series Forecasting: A Survey

arXiv 2503.10198 ↗First posted 13 Mar 2025cs.LG cs.AIcited by 10

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Abstract (arXiv)

Time series forecasting (TSF) has long been a crucial task in both industry and daily life. Most classical statistical models may have certain limitations when applied to practical scenarios in fields such as energy, healthcare, traffic, meteorology, and economics, especially when high accuracy is required. With the continuous development of deep learning, numerous new models have emerged in the field of time series forecasting in recent years. However, existing surveys have not provided a unified summary of the wide range of model architectures in this field, nor have they given detailed summaries of works in feature extraction and datasets. To address this gap, in this review, we comprehensively study the previous works and summarize the general paradigms of Deep Time Series Forecasting (DTSF) in terms of model architectures. Besides, we take an innovative approach by focusing on the composition of time series and systematically explain important feature extraction methods. Additionally, we provide an overall compilation of datasets from various domains in existing works. Finally, we systematically emphasize the significant challenges faced and future research directions in this field.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 110 of its 260 references that are in the map. First posted on arXiv 13 Mar 2025.

Builds on

110 of 260 references.

References · 110

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38 more
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50 most cited of 110 All 110 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 7.

10 papers.

  1. 2026Expectations vs. Realities: The Cost of MSE-Optimal Forecasting Under Conditional Uncertaintycited by 3
  2. 2025Numerical Investigation of Sequence Modeling Theory using Controllable Memory Functionscited by 1
  3. 2026GroupSegment-SHAP: Shapley Value Explanations with Group-Segment Players for Multivariate Time Seriescited by 1
  4. 2026DSPR: Dual-Stream Physics-Residual Networks for Trustworthy Industrial Time Series Forecastingcited by 1
  5. 2025ML-Based Bidding Price Prediction for Pay-As-Bid Ancillary Services Markets: A Use Case in the German Control Reserve Marketcited by 0
  6. 2025Predicting the Unpredictable: Reproducible BiLSTM Forecasting of Incident Counts in the Global Terrorism Database (GTD)cited by 0
  7. 2026DeMa: Dual-Path Delay-Aware Mamba for Efficient Multivariate Time Series Analysiscited by 0
  8. 2026Forecasting Individual NetFlows using a Predictive Masked Graph Autoencodercited by 0
  9. 2026An Empirical Benchmark of Deep Time-Series Models for Smart Meter Energy Forecastingcited by 0
  10. 2026Quantifying Event Impacts on Time Series via Multiscale Contrastive Learningcited by 0