mlsanityData to 7 Oct 2026

Deep Factors with Gaussian Processes for Forecasting

arXiv 1812.00098 ↗First posted 30 Nov 2018stat.ML cs.LGcited by 16

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Abstract (arXiv)

A large collection of time series poses significant challenges for classical and neural forecasting approaches. Classical time series models fail to fit data well and to scale to large problems, but succeed at providing uncertainty estimates. The converse is true for deep neural networks. In this paper, we propose a hybrid model that incorporates the benefits of both approaches. Our new method is data-driven and scalable via a latent, global, deep component. It also handles uncertainty through a local classical Gaussian Process model. Our experiments demonstrate that our method obtains higher accuracy than state-of-the-art methods.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). First posted on arXiv 30 Nov 2018.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 4; 2022: 3; 2023: 3; 2024: 3; 2025: 2; 2026: 1.

16 papers.

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  1. 2021Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecastingcited by 1,654
  2. 2022Non-stationary Transformers: Exploring the Stationarity in Time Series Forecastingcited by 385
  3. 2023Generative Time Series Forecasting with Diffusion, Denoise, and Disentanglementcited by 89
  4. 2022Criteria for Classifying Forecasting Methodscited by 48
  5. 2021Causal Inference for Time series Analysis: Problems, Methods and Evaluationcited by 42
  6. 2022FECAM: Frequency Enhanced Channel Attention Mechanism for Time Series Forecastingcited by 15
  7. 2021MixSeq: Connecting Macroscopic Time Series Forecasting with Microscopic Time Series Datacited by 9
  8. 2021TCCT: Tightly-Coupled Convolutional Transformer on Time Series Forecastingcited by 8
  9. 2023Does Long-Term Series Forecasting Need Complex Attention and Extra Long Inputs?cited by 5
  10. 2024WEITS: A Wavelet-enhanced residual framework for interpretable time series forecastingcited by 3
  11. 2023Fine-grained Forecasting Models Via Gaussian Process Blurring Effectcited by 2
  12. 2024An Efficient and Generalizable Symbolic Regression Method for Time Series Analysiscited by 2
4 more
  1. 2024A nudge to the truth: atom conservation as a hard constraint in models of atmospheric composition using an uncertainty-weighted correctioncited by 0
  2. 2025CANet: ChronoAdaptive Network for Enhanced Long-Term Time Series Forecasting under Non-Stationaritycited by 0
  3. 2025Structured Noise Modeling for Enhanced Time-Series Forecastingcited by 0
  4. 2026FISformer: Replacing Self-Attention with a Fuzzy Inference System in Transformer Models for Time Series Forecastingcited by 0