# Deep Switching Auto-Regressive Factorization:Application to Time Series Forecasting

arXiv: https://arxiv.org/abs/2009.05135 · HTML: https://mlsanity.com/paper/2009.05135/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Sep 2020.

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

- [Generative Time Series Forecasting with Diffusion, Denoise, and Disentanglement](https://mlsanity.com/paper/2301.03028/) (2023)
- [Learning Fast and Slow for Online Time Series Forecasting](https://mlsanity.com/paper/2202.11672/) (2022)
- [A Systematic Review for Transformer-based Long-term Series Forecasting](https://mlsanity.com/paper/2310.20218/) (2023)
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- [DipLLM: Fine-Tuning LLM for Strategic Decision-making in Diplomacy](https://mlsanity.com/paper/2506.09655/) (2025)
- [Switching Autoregressive Low-rank Tensor Models](https://mlsanity.com/paper/2306.03291/) (2023)
- [Equivariant Deep Dynamical Model for Motion Prediction](https://mlsanity.com/paper/2111.01892/) (2021)
- [Dynamical Deep Generative Latent Modeling of 3D Skeletal Motion](https://mlsanity.com/paper/2106.10393/) (2021)
- [PRRS Outbreak Prediction via Deep Switching Auto-Regressive Factorization Modeling](https://mlsanity.com/paper/2110.03147/) (2021)
- [Discovering group dynamics in coordinated time series via hierarchical recurrent switching-state models](https://mlsanity.com/paper/2401.14973/) (2024)
- [Switching Recurrent Kalman Networks](https://mlsanity.com/paper/2111.08291/) (2021)
- [Blood Glucose Control Via Pre-trained Counterfactual Invertible Neural Networks](https://mlsanity.com/paper/2405.17458/) (2024)
- [TSP-OCS: A Time-Series Prediction for Optimal Camera Selection in Multi-Viewpoint Surgical Video Analysis](https://mlsanity.com/paper/2504.06527/) (2025)
- [Locating acts of mechanistic reasoning in student team conversations with mechanistic machine learning](https://mlsanity.com/paper/2604.21870/) (2026)
- [AdaKoop: Efficient Modeling of Nonlinear Dynamics from Nonstationary Data Streams with Koopman Operator Regression](https://mlsanity.com/paper/2606.04930/) (2026)

## Abstract (arXiv)

We introduce deep switching auto-regressive factorization (DSARF), a deep generative model for spatio-temporal data with the capability to unravel recurring patterns in the data and perform robust short- and long-term predictions. Similar to other factor analysis methods, DSARF approximates high dimensional data by a product between time dependent weights and spatially dependent factors. These weights and factors are in turn represented in terms of lower dimensional latent variables that are inferred using stochastic variational inference. DSARF is different from the state-of-the-art techniques in that it parameterizes the weights in terms of a deep switching vector auto-regressive likelihood governed with a Markovian prior, which is able to capture the non-linear inter-dependencies among weights to characterize multimodal temporal dynamics. This results in a flexible hierarchical deep generative factor analysis model that can be extended to (i) provide a collection of potentially interpretable states abstracted from the process dynamics, and (ii) perform short- and long-term vector time series prediction in a complex multi-relational setting. Our extensive experiments, which include simulated data and real data from a wide range of applications such as climate change, weather forecasting, traffic, infectious disease spread and nonlinear physical systems attest the superior performance of DSARF in terms of long- and short-term prediction error, when compared with the state-of-the-art methods.