# Deep Factors with Gaussian Processes for Forecasting

arXiv: https://arxiv.org/abs/1812.00098 · HTML: https://mlsanity.com/paper/1812.00098/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Nov 2018.

## Built on by (most cited first, 16 of 16)

- [Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting](https://mlsanity.com/paper/2106.13008/) (2021)
- [Non-stationary Transformers: Exploring the Stationarity in Time Series Forecasting](https://mlsanity.com/paper/2205.14415/) (2022)
- [Generative Time Series Forecasting with Diffusion, Denoise, and Disentanglement](https://mlsanity.com/paper/2301.03028/) (2023)
- [Criteria for Classifying Forecasting Methods](https://mlsanity.com/paper/2212.03523/) (2022)
- [Causal Inference for Time series Analysis: Problems, Methods and Evaluation](https://mlsanity.com/paper/2102.05829/) (2021)
- [FECAM: Frequency Enhanced Channel Attention Mechanism for Time Series Forecasting](https://mlsanity.com/paper/2212.01209/) (2022)
- [MixSeq: Connecting Macroscopic Time Series Forecasting with Microscopic Time Series Data](https://mlsanity.com/paper/2110.14354/) (2021)
- [TCCT: Tightly-Coupled Convolutional Transformer on Time Series Forecasting](https://mlsanity.com/paper/2108.12784/) (2021)
- [Does Long-Term Series Forecasting Need Complex Attention and Extra Long Inputs?](https://mlsanity.com/paper/2306.05035/) (2023)
- [WEITS: A Wavelet-enhanced residual framework for interpretable time series forecasting](https://mlsanity.com/paper/2405.10877/) (2024)
- [Fine-grained Forecasting Models Via Gaussian Process Blurring Effect](https://mlsanity.com/paper/2312.14280/) (2023)
- [An Efficient and Generalizable Symbolic Regression Method for Time Series Analysis](https://mlsanity.com/paper/2409.03986/) (2024)
- [A nudge to the truth: atom conservation as a hard constraint in models of atmospheric composition using an uncertainty-weighted correction](https://mlsanity.com/paper/2408.16109/) (2024)
- [CANet: ChronoAdaptive Network for Enhanced Long-Term Time Series Forecasting under Non-Stationarity](https://mlsanity.com/paper/2504.17913/) (2025)
- [Structured Noise Modeling for Enhanced Time-Series Forecasting](https://mlsanity.com/paper/2511.19657/) (2025)
- [FISformer: Replacing Self-Attention with a Fuzzy Inference System in Transformer Models for Time Series Forecasting](https://mlsanity.com/paper/2603.21724/) (2026)

## 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.