# Spatio-temporal DeepKriging in PyTorch: A Supplementary Application to Precipitation Data for Interpolation and Probabilistic Forecasting

arXiv: https://arxiv.org/abs/2509.12708 · HTML: https://mlsanity.com/paper/2509.12708/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 11 references that are in the map.
- First posted on arXiv 16 Sep 2025.

## Builds on (most cited first, 2 of 2)

- [Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles](https://mlsanity.com/paper/1612.01474/) (2016)
- [Spatio-temporal DeepKriging for Interpolation and Probabilistic Forecasting](https://mlsanity.com/paper/2306.11472/) (2023)

## Built on by (most cited first, 1 of 1)

- [Spatio-temporal modeling and forecasting with Fourier neural operators](https://mlsanity.com/paper/2601.01813/) (2026)

## Abstract (arXiv)

A detailed analysis of precipitation data over Europe is presented, with a focus on interpolation and forecasting applications. A Spatio-temporal DeepKriging (STDK) framework has been implemented using the PyTorch platform to achieve these objectives. The proposed model is capable of handling spatio-temporal irregularities while generating high-resolution interpolations and multi-step forecasts. Reproducible code modules have been developed as standalone PyTorch implementations for the interpolationfootnote[2]Interpolation - https://github.com/pratiknag/Spatio-temporalDeepKriging-Pytorch.git and forecastingfootnote[3]Forecasting - https://github.com/pratiknag/pytorch-convlstm.git, facilitating broader application to similar climate datasets. The effectiveness of this approach is demonstrated through extensive evaluation on daily precipitation measurements, highlighting predictive performance and robustness.