# Stanza: A Nonlinear State Space Model for Probabilistic Inference in Non-Stationary Time Series

arXiv: https://arxiv.org/abs/2006.06553 · HTML: https://mlsanity.com/paper/2006.06553/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jun 2020.

## Built on by (most cited first, 6 of 6)

- [TACTiS: Transformer-Attentional Copulas for Time Series](https://mlsanity.com/paper/2202.03528/) (2022)
- [TACTiS-2: Better, Faster, Simpler Attentional Copulas for Multivariate Time Series](https://mlsanity.com/paper/2310.01327/) (2023)
- [Time Series Analysis and Modeling to Forecast: a Survey](https://mlsanity.com/paper/2104.00164/) (2021)
- [Deep Probabilistic Time Series Forecasting using Augmented Recurrent Input for Dynamic Systems](https://mlsanity.com/paper/2106.05848/) (2021)
- [DynaConF: Dynamic Forecasting of Non-Stationary Time Series](https://mlsanity.com/paper/2209.08411/) (2022)
- [Perceiver-based CDF Modeling for Time Series Forecasting](https://mlsanity.com/paper/2310.01720/) (2023)

## Abstract (arXiv)

Time series with long-term structure arise in a variety of contexts and capturing this temporal structure is a critical challenge in time series analysis for both inference and forecasting settings. Traditionally, state space models have been successful in providing uncertainty estimates of trajectories in the latent space. More recently, deep learning, attention-based approaches have achieved state of the art performance for sequence modeling, though often require large amounts of data and parameters to do so. We propose Stanza, a nonlinear, non-stationary state space model as an intermediate approach to fill the gap between traditional models and modern deep learning approaches for complex time series. Stanza strikes a balance between competitive forecasting accuracy and probabilistic, interpretable inference for highly structured time series. In particular, Stanza achieves forecasting accuracy competitive with deep LSTMs on real-world datasets, especially for multi-step ahead forecasting.