# A Comprehensive Survey of Deep Learning for Time Series Forecasting: Architectural Diversity and Open Challenges

arXiv: https://arxiv.org/abs/2411.05793 · HTML: https://mlsanity.com/paper/2411.05793/

## Key facts

- Cited by 47 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Oct 2024.

## Built on by (most cited first, 30 of 47)

- [Patch-wise Structural Loss for Time Series Forecasting](https://mlsanity.com/paper/2503.00877/) (2025)
- [TRACE: Grounding Time Series in Context for Multimodal Embedding and Retrieval](https://mlsanity.com/paper/2506.09114/) (2025)
- [Flow-based Conformal Prediction for Multi-dimensional Time Series](https://mlsanity.com/paper/2502.05709/) (2025)
- [Decentralized Attention Fails Centralized Signals: Rethinking Transformers for Medical Time Series](https://mlsanity.com/paper/2602.18473/) (2026)
- [Giving Sensors a Voice: Multimodal JEPA for Semantic Time-Series Embeddings](https://mlsanity.com/paper/2605.31580/) (2026)
- [TFKAN: Time-Frequency KAN for Long-Term Time Series Forecasting](https://mlsanity.com/paper/2506.12696/) (2025)
- [TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting](https://mlsanity.com/paper/2511.18539/) (2025)
- [Time to Embed: Unlocking Foundation Models for Time Series with Channel Descriptions](https://mlsanity.com/paper/2505.14543/) (2025)
- [Position: The Inevitable End of One-Architecture-Fits-All-Domains in Time Series Forecasting](https://mlsanity.com/paper/2602.01736/) (2026)
- [Explainable Artificial Intelligence Techniques for Interpretation of Food Models: a Review](https://mlsanity.com/paper/2504.10527/) (2025)
- [Causal Graph Fuzzy LLMs: A First Introduction and Applications in Time Series Forecasting](https://mlsanity.com/paper/2507.17016/) (2025)
- [Variability Aware Recursive Neural Network (VARNN): A Residual-Memory Model for Capturing Temporal Deviation in Sequence Regression Modeling](https://mlsanity.com/paper/2510.08944/) (2025)
- [Time Series Foundation Models for Process Model Forecasting](https://mlsanity.com/paper/2512.07624/) (2025)
- [CAPS: Unifying Attention, Recurrence, and Alignment in Transformer-based Time Series Forecasting](https://mlsanity.com/paper/2602.02729/) (2026)
- [Tuning the burn-in phase in training recurrent neural networks improves their performance](https://mlsanity.com/paper/2602.10911/) (2026)
- [WaveMoE: A Wavelet-Enhanced Mixture-of-Experts Foundation Model for Time Series Forecasting](https://mlsanity.com/paper/2604.10544/) (2026)
- [L-Drive: Beyond a Single Mapping-Latent Context Drives Time Series Forecasting](https://mlsanity.com/paper/2605.17730/) (2026)
- [Reasoning through Verifiable Forecast Actions: Consistency-Grounded RL for Financial LLMs](https://mlsanity.com/paper/2605.21975/) (2026)
- [Factorize to Generalize: Retrieval-Guided Invariant-Dynamic Decomposition for Time Series Forecasting](https://mlsanity.com/paper/2605.24911/) (2026)
- [When to Trust, How to Distill: Multi-Foundation Model Guidance for Lightweight, Robust Scientific Time Series Forecasting](https://mlsanity.com/paper/2606.19363/) (2026)
- [Tabby: An Open Pretraining Recipe for Time Series Foundation Models](https://mlsanity.com/paper/2609.13956/) (2026)
- [Forecast-Then-Optimize Deep Learning Methods](https://mlsanity.com/paper/2506.13036/) (2025)
- [Recurrent Expansion: A Pathway Toward the Next Generation of Deep Learning](https://mlsanity.com/paper/2507.08828/) (2025)
- [TS-P2CL: Plug-and-Play Dual Contrastive Learning for Vision-Guided Medical Time Series Classification](https://mlsanity.com/paper/2509.17802/) (2025)
- [Lossless Compression: A New Benchmark for Time Series Model Evaluation](https://mlsanity.com/paper/2509.21002/) (2025)
- [ReNF: Rethinking the Design of Neural Long-Term Time Series Forecasters](https://mlsanity.com/paper/2509.25914/) (2025)
- [HTMformer: Hybrid Time and Multivariate Transformer for Time Series Forecasting](https://mlsanity.com/paper/2510.07084/) (2025)
- [InvDec: Inverted Decoder for Multivariate Time Series Forecasting with Separated Temporal and Variate Modeling](https://mlsanity.com/paper/2510.20302/) (2025)
- [AWEMixer: Adaptive Wavelet-Enhanced Mixer Network for Long-Term Time Series Forecasting](https://mlsanity.com/paper/2511.04722/) (2025)
- [Weighted Contrastive Learning for Anomaly-Aware Time-Series Forecasting](https://mlsanity.com/paper/2512.07569/) (2025)

## Abstract (arXiv)

Time series forecasting is a critical task that provides key information for decision-making. After traditional statistical and machine learning approaches, various fundamental deep learning architectures such as MLPs, CNNs, RNNs, and GNNs have been developed. However, the structural limitations caused by the inductive biases of each deep learning architecture constrained their performance. Transformer models, which excel at handling long-term dependencies, have become significant architectural components for time series forecasting. However, recent research has shown that alternatives such as simple linear layers can outperform Transformers. These findings have opened up new possibilities for using diverse architectures, ranging from fundamental deep learning models to emerging architectures and hybrid approaches. In this context, architectural modeling of time series forecasting has now entered a renaissance. This survey not only provides a historical context for time series forecasting but also offers comprehensive and timely analysis of the movement toward architectural diversification. By comparing and re-examining deep learning models, we uncover new perspectives and present recent trends, including hybrid, diffusion, Mamba, and foundation models. By focusing on the inherent characteristics of time series data, we also address open challenges that have gained attention in time series forecasting, such as channel dependency, distribution shift, causality, and feature extraction. These contributions help lower entry barriers for newcomers by providing a systematic understanding of the diverse research areas in time series forecasting (TSF), while offering seasoned researchers broader perspectives and new opportunities through in-depth exploration of TSF challenges. (Shortened due to arXiv's 1,920-character limit. Full version in the paper.)