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

Farnoosh, Amirreza; Azari, Bahar; Ostadabbas, Sarah

doi:10.48550/arxiv.2009.05135

Cited by 3 publications

(10 citation statements)

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“…We further applied the DS 3 M to several real-world datasets covering a variety of applications such as health care, transportation, energy, econometrics. Both simulations and real data analysis demonstrate that the DS 3 M captures the switching regimes well and achieves competitive prediction accuracy compared with several state-of-the-art methods, including GRU, SRNN [22], DSARF [27] and SNLDS [28]. Specifically, SRNN can be considered as our model without discrete latent variables.…”

Section: Methodsmentioning

confidence: 94%

“…The SNLDS model [28] parametrizes both the emission and transition functions with nonlinear neural networks. The DSARF [27] approximates the high-dimensional time series with the dynamics of factors and weights that are guided by the switching latent variables.…”

Section: Related Workmentioning

confidence: 99%

“…In the simulation, we considered a 10-dimensional time series y t = W z t + v t , where W ∈ R 10×3 , v t ∼ N (0, 0.5I 10 ). The same dataset was used in [27]. Similar to the toy example, we set x t = y t−1 .…”

Section: Simulationsmentioning

confidence: 99%

“…Though powerful, it is challenging to understand how the dynamics are evolving, without knowledge of discrete regimes. It stimulates the development of deep SSMs which incorporates discrete latent variables for an interpretable inference of nonlinear time series with regime switching, see SVAE [26], DSARF [27] and SNLDS [28] which have both discrete and continuous latent variables, rHSMM [29] and HSMM [30] which have two sets of discrete latent variables.…”

Section: Introductionmentioning

confidence: 99%

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Deep Switching State Space Model (DS$^3$M) for Nonlinear Time Series Forecasting with Regime Switching

Xu,

Chen

2021

Preprint

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We propose a deep switching state space model (DS 3 M) for efficient inference and forecasting of nonlinear time series with irregularly switching among various regimes. The switching among regimes is captured by both discrete and continuous latent variables with recurrent neural networks. The model is estimated with variational inference using a reparameterization trick. We test the approach on a variety of simulated and real datasets. In all cases, DS 3 M achieves competitive performance compared to several state-of-the-art methods (e.g. GRU, SRNN, DSARF, SNLDS), with superior forecasting accuracy, convincing interpretability of the discrete latent variables, and powerful representation of the continuous latent variables for different kinds of time series. Specifically, the MAPE values increase by 0.09% to 15.71% against the second-best performing alternative models.

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Section: Methodsmentioning

confidence: 94%

Section: Related Workmentioning

confidence: 99%

Section: Simulationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Deep Switching State Space Model (DS$^3$M) for Nonlinear Time Series Forecasting with Regime Switching

Xu,

Chen

2021

Preprint

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“…[Qiu et al, 2020] introduces a novel inference technique that accounts for multimodality. Other works employ the idea of switching regimes incorporated with deep learning, such as [Johnson et al, 2016, Farnoosh et al, 2020, Dai et al, 2016, Liu et al, 2018. These models assume the Markov assumption on the state Figure 1: The architecture of the Switching Recurrent Kalman Network.…”

Section: Related Workmentioning

confidence: 99%

Switching Recurrent Kalman Networks

Nguyen-Quynh¹,

Becker²,

Qiu³

et al. 2021

Preprint

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Forecasting driving behavior or other sensor measurements is an essential component of autonomous driving systems. Often real-world multivariate time series data is hard to model because the underlying dynamics are nonlinear and the observations are noisy. In addition, driving data can often be multimodal in distribution, meaning that there are distinct predictions that are likely, but averaging can hurt model performance. To address this, we propose the Switching Recurrent Kalman Network (SRKN) for efficient inference and prediction on nonlinear and multimodal time-series data. The model switches among several Kalman filters that model different aspects of the dynamics in a factorized latent state. We empirically test the resulting scalable and interpretable deep state-space model on toy data sets and real driving data from taxis in Porto. In all cases, the model can capture the multimodal nature of the dynamics in the data.

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Generative Time Series Forecasting with Diffusion, Denoise, and Disentanglement

Li¹,

Lu²,

Wang³

et al. 2023

Preprint

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Time series forecasting has been a widely explored task of great importance in many applications. However, it is common that real-world time series data are recorded in a short time period, which results in a big gap between the deep model and the limited and noisy time series. In this work, we propose to address the time series forecasting problem with generative modeling and propose a bidirectional variational auto-encoder (BVAE) equipped with diffusion, denoise, and disentanglement, namely D 3 VAE. Specifically, a coupled diffusion probabilistic model is proposed to augment the time series data without increasing the aleatoric uncertainty and implement a more tractable inference process with BVAE. To ensure the generated series move toward the true target, we further propose to adapt and integrate the multiscale denoising score matching into the diffusion process for time series forecasting. In addition, to enhance the interpretability and stability of the prediction, we treat the latent variable in a multivariate manner and disentangle them on top of minimizing total correlation. Extensive experiments on synthetic and real-world data show that D 3 VAE outperforms competitive algorithms with remarkable margins. Our implementation is available at https://github.com/ PaddlePaddle/PaddleSpatial/tree/main/research/D3VAE.

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

Cited by 3 publications

References 0 publications

Deep Switching State Space Model (DS$^3$M) for Nonlinear Time Series Forecasting with Regime Switching

Deep Switching State Space Model (DS$^3$M) for Nonlinear Time Series Forecasting with Regime Switching

Switching Recurrent Kalman Networks

Generative Time Series Forecasting with Diffusion, Denoise, and Disentanglement

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