# Deep Forecast: Deep Learning-based Spatio-Temporal Forecasting

arXiv: https://arxiv.org/abs/1707.08110 · HTML: https://mlsanity.com/paper/1707.08110/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jul 2017.

## Built on by (most cited first, 13 of 13)

- [Urban Generative Intelligence (UGI): A Foundational Platform for Agents in Embodied City Environment](https://mlsanity.com/paper/2312.11813/) (2023)
- [Spatio-temporal graph neural networks for multi-site PV power forecasting](https://mlsanity.com/paper/2107.13875/) (2021)
- [Deep Time Series Forecasting with Shape and Temporal Criteria](https://mlsanity.com/paper/2104.04610/) (2021)
- [A Systematic Literature Review of Spatio-Temporal Graph Neural Network Models for Time Series Forecasting and Classification](https://mlsanity.com/paper/2410.22377/) (2024)
- [Graph Neural Network for spatiotemporal data: methods and applications](https://mlsanity.com/paper/2306.00012/) (2023)
- [Deep Spatio-Temporal Wind Power Forecasting](https://mlsanity.com/paper/2109.14530/) (2021)
- [Wind Park Power Prediction: Attention-Based Graph Networks and Deep Learning to Capture Wake Losses](https://mlsanity.com/paper/2201.03229/) (2022)
- [Beyond Ensemble Averages: Leveraging Climate Model Ensembles for Subseasonal Forecasting](https://mlsanity.com/paper/2211.15856/) (2022)
- [DeepMIDE: A Multi-Output Spatio-Temporal Method for Ultra-Scale Offshore Wind Energy Forecasting](https://mlsanity.com/paper/2410.20166/) (2024)
- [From Optimization to Prediction: Transformer-Based Path-Flow Estimation to the Traffic Assignment Problem](https://mlsanity.com/paper/2510.19889/) (2025)
- [A Tale of Two Cities: Data and Configuration Variances in Robust Deep Learning](https://mlsanity.com/paper/2211.10012/) (2022)
- [It is all Connected: A New Graph Formulation for Spatio-Temporal Forecasting](https://mlsanity.com/paper/2303.13177/) (2023)
- [A Novel Correlation-optimized Deep Learning Method for Wind Speed Forecast](https://mlsanity.com/paper/2306.01986/) (2023)

## Abstract (arXiv)

The paper presents a spatio-temporal wind speed forecasting algorithm using Deep Learning (DL)and in particular, Recurrent Neural Networks(RNNs). Motivated by recent advances in renewable energy integration and smart grids, we apply our proposed algorithm for wind speed forecasting. Renewable energy resources (wind and solar)are random in nature and, thus, their integration is facilitated with accurate short-term forecasts. In our proposed framework, we model the spatiotemporal information by a graph whose nodes are data generating entities and its edges basically model how these nodes are interacting with each other. One of the main contributions of our work is the fact that we obtain forecasts of all nodes of the graph at the same time based on one framework. Results of a case study on recorded time series data from a collection of wind mills in the north-east of the U.S. show that the proposed DL-based forecasting algorithm significantly improves the short-term forecasts compared to a set of widely-used benchmarks models.