# Foundations of Sequence-to-Sequence Modeling for Time Series

arXiv: https://arxiv.org/abs/1805.03714 · HTML: https://mlsanity.com/paper/1805.03714/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 May 2018.

## Built on by (most cited first, 15 of 15)

- [Monash Time Series Forecasting Archive](https://mlsanity.com/paper/2105.06643/) (2021)
- [Physics-Guided Deep Learning for Dynamical Systems: A Survey](https://mlsanity.com/paper/2107.01272/) (2021)
- [Learning Quantile Functions without Quantile Crossing for Distribution-free Time Series Forecasting](https://mlsanity.com/paper/2111.06581/) (2021)
- [Unsupervised Flow-Aligned Sequence-to-Sequence Learning for Video Restoration](https://mlsanity.com/paper/2205.10195/) (2022)
- [Deep Time Series Forecasting with Shape and Temporal Criteria](https://mlsanity.com/paper/2104.04610/) (2021)
- [Causal Explanations for Sequential Decision-Making in Multi-Agent Systems](https://mlsanity.com/paper/2302.10809/) (2023)
- [Parameter Efficient Deep Probabilistic Forecasting](https://mlsanity.com/paper/2112.02905/) (2021)
- [Predicting Hate Intensity of Twitter Conversation Threads](https://mlsanity.com/paper/2206.08406/) (2022)
- [Synergetic Learning of Heterogeneous Temporal Sequences for Multi-Horizon Probabilistic Forecasting](https://mlsanity.com/paper/2102.00431/) (2021)
- [Semiparametric inference for impulse response functions using double/debiased machine learning](https://mlsanity.com/paper/2411.10009/) (2024)
- [Bayesian inference and neural estimation of acoustic wave propagation](https://mlsanity.com/paper/2305.17749/) (2023)
- [Improved Long Short-Term Memory-based Wastewater Treatment Simulators for Deep Reinforcement Learning](https://mlsanity.com/paper/2403.15091/) (2024)
- [SPformer: A Transformer Based DRL Decision Making Method for Connected Automated Vehicles](https://mlsanity.com/paper/2409.15105/) (2024)
- [Hierarchical Bayesian Regression for Multi-Location Sales Transaction Forecasting](https://mlsanity.com/paper/2306.17795/) (2023)
- [Interpretability in Deep Time Series Models Demands Semantic Alignment](https://mlsanity.com/paper/2602.02239/) (2026)

## Abstract (arXiv)

The availability of large amounts of time series data, paired with the performance of deep-learning algorithms on a broad class of problems, has recently led to significant interest in the use of sequence-to-sequence models for time series forecasting. We provide the first theoretical analysis of this time series forecasting framework. We include a comparison of sequence-to-sequence modeling to classical time series models, and as such our theory can serve as a quantitative guide for practitioners choosing between different modeling methodologies.