Time Series Forecasting & Temporal Models Reading List

Curated by Mouhssine Rifaki | Stanford Electrical Engineering | Last updated August 2026

From classical probabilistic models to transformer-based forecasters, this list traces how time series prediction has scaled from single-step autoregression to long-horizon, multivariate forecasting with interpretable architectures and decomposition.

Time Series Forecasting & Temporal Models: 10 key papers

1. DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks
   Salinas et al. arXiv 2017.
2. Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting
   Lim et al. arXiv 2019.
3. Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting
   Zhou et al. arXiv 2020.
4. Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting
   Wu et al. arXiv 2021.
5. Are Transformers Effective for Time Series Forecasting?
   Zeng et al. arXiv 2022.
6. N-BEATS: Neural basis expansion analysis for interpretable time series forecasting
   Oreshkin et al. arXiv 2019.
7. A Time Series is Worth 64 Words: Long-term Forecasting with Transformers
   Nie et al. arXiv 2022.
8. TimeGPT-1
   Garza et al. arXiv 2023.
9. FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting
   Zhou et al. arXiv 2022.
10. Deep State Space Models for Time Series Forecasting
    Rangapuram et al. Advances in Neural Information Processing Systems 2018.

Related reading lists

• Neural ODEs & Continuous-Time Models
• State Space Models & Structured SSMs
• System Identification & Learning Dynamical Systems
• Efficient Transformers
• Transformers for Sequential Decision Making
• Long-Context & Memory Transformers
• Hierarchical RL & Options Framework
• In-Context RL & Prompting