mlsanityData to 14 Oct 2026

SSDNet: State Space Decomposition Neural Network for Time Series Forecasting

arXiv 2112.10251 ↗First posted 19 Dec 2021cs.LGcited by 15

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Abstract (arXiv)

In this paper, we present SSDNet, a novel deep learning approach for time series forecasting. SSDNet combines the Transformer architecture with state space models to provide probabilistic and interpretable forecasts, including trend and seasonality components and previous time steps important for the prediction. The Transformer architecture is used to learn the temporal patterns and estimate the parameters of the state space model directly and efficiently, without the need for Kalman filters. We comprehensively evaluate the performance of SSDNet on five data sets, showing that SSDNet is an effective method in terms of accuracy and speed, outperforming state-of-the-art deep learning and statistical methods, and able to provide meaningful trend and seasonality components.

Key facts. Cited by 15 papers in the map (data through 14 Oct 2026). Builds on 13 of its 22 references that are in the map. First posted on arXiv 19 Dec 2021.

Builds on

13 of 22 references.

References · 13

  1. 2017Attention Is All You Needcited by 60,361
  2. 2020Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecastingcited by 2,169
  3. 2017DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networkscited by 1,013
  4. 2017Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networkscited by 879
  5. 2019N-BEATS: Neural basis expansion analysis for interpretable time series forecastingcited by 753
  6. 2019Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecastingcited by 607
  7. 2015BinaryConnect: Training Deep Neural Networks with binary weights during propagationscited by 592
  8. 2015Deep Kalman Filterscited by 166
  9. 2019Deep Factors for Forecastingcited by 86
  10. 2019Recurrent Kalman Networks: Factorized Inference in High-Dimensional Deep Feature Spacescited by 67
  11. 2019Exploring Interpretable LSTM Neural Networks over Multi-Variable Datacited by 42
  12. 2017Identification of Gaussian Process State Space Modelscited by 31
1 more
  1. 2021Learning Interpretable Deep State Space Model for Probabilistic Time Series Forecastingcited by 29

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 3; 2023: 4; 2024: 6; 2025: 1; 2026: 1.

15 papers.

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  1. 2022Transformers in Time Series: A Surveycited by 497
  2. 2023Disentangling Structured Components: Towards Adaptive, Interpretable and Scalable Time Series Forecastingcited by 19
  3. 2024Bi-Mamba+: Bidirectional Mamba for Time Series Forecastingcited by 15
  4. 2023A Systematic Review for Transformer-based Long-term Series Forecastingcited by 10
  5. 2022Probabilistic Decomposition Transformer for Time Series Forecastingcited by 6
  6. 2024Quantformer: from attention to profit with a quantitative transformer trading strategycited by 6
  7. 2024XXLTraffic: Expanding and Extremely Long Traffic forecasting beyond test adaptationcited by 6
  8. 2024Conformal time series decomposition with component-wise exchangeabilitycited by 3
  9. 2023Progressive Neural Network for Multi-Horizon Time Series Forecastingcited by 1
  10. 2024Hybrid Quantum-inspired Resnet and Densenet for Pattern Recognitioncited by 1
  11. 2024KEDformer:Knowledge Extraction Seasonal Trend Decomposition for Long-term Sequence Predictioncited by 1
  12. 2025Spatially-Enhanced Temporal Fusion Transformer: Interpretable Multi-Output Prediction for Parametric Dynamical Systems with Time-Varying Inputscited by 1
3 more
  1. 2026From XXLTraffic to EvoXXLTraffic: Scaling Traffic Forecasting to Sensor-Evolving Networkscited by 1
  2. 2022Improving Accuracy Without Losing Interpretability: A ML Approach for Time Series Forecastingcited by 0
  3. 2023AMLNet: Adversarial Mutual Learning Neural Network for Non-AutoRegressive Multi-Horizon Time Series Forecastingcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2023Effectively Modeling Time Series with Simple Discrete State Spaces
  2. 2026A Decomposition-based State Space Model for Multivariate Time-Series Forecasting
  3. 2024A Comprehensive Survey of Deep Learning for Time Series Forecasting: Architectural Diversity and Open Challenges
  4. 2024SDE: A Simplified and Disentangled Dependency Encoding Framework for State Space Models in Time Series Forecasting
  5. 2024Deep Learning-based Approaches for State Space Models: A Selective Review
  6. 2026DMamba: Decomposition-enhanced Mamba for Time Series Forecasting
  7. 2021Stochastic Recurrent Neural Network for Multistep Time Series Forecasting
  8. 2024Evaluating Time-Series Training Dataset through Lens of Spectrum in Deep State Space Models