# Forecaster: A Graph Transformer for Forecasting Spatial and Time-Dependent Data

arXiv: https://arxiv.org/abs/1909.04019 · HTML: https://mlsanity.com/paper/1909.04019/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Sep 2019.

## Built on by (most cited first, 11 of 11)

- [Graph Neural Network for Traffic Forecasting: A Survey](https://mlsanity.com/paper/2101.11174/) (2021)
- [Regularized Graph Structure Learning with Semantic Knowledge for Multi-variates Time-Series Forecasting](https://mlsanity.com/paper/2210.06126/) (2022)
- [Spatio-Temporal Graph Neural Networks: A Survey](https://mlsanity.com/paper/2301.10569/) (2023)
- [A Systematic Review for Transformer-based Long-term Series Forecasting](https://mlsanity.com/paper/2310.20218/) (2023)
- [A Lightweight and Accurate Spatial-Temporal Transformer for Traffic Forecasting](https://mlsanity.com/paper/2201.00008/) (2021)
- [A Graph and Attentive Multi-Path Convolutional Network for Traffic Prediction](https://mlsanity.com/paper/2205.15218/) (2022)
- [STGIN: A Spatial Temporal Graph-Informer Network for Long Sequence Traffic Speed Forecasting](https://mlsanity.com/paper/2210.01799/) (2022)
- [Network-Constrained Policy Optimization for Adaptive Multi-agent Vehicle Routing](https://mlsanity.com/paper/2510.26089/) (2025)
- [GSA-Forecaster: Forecasting Graph-Based Time-Dependent Data with Graph Sequence Attention](https://mlsanity.com/paper/2104.05914/) (2021)
- [KENN: Enhancing Deep Neural Networks by Leveraging Knowledge for Time Series Forecasting](https://mlsanity.com/paper/2202.03903/) (2022)
- [A Unified Framework for Next-Gen Urban Forecasting via LLM-driven Dependency Retrieval and GeoTransformer](https://mlsanity.com/paper/2408.08852/) (2024)

## Abstract (arXiv)

Spatial and time-dependent data is of interest in many applications. This task is difficult due to its complex spatial dependency, long-range temporal dependency, data non-stationarity, and data heterogeneity. To address these challenges, we propose Forecaster, a graph Transformer architecture. Specifically, we start by learning the structure of the graph that parsimoniously represents the spatial dependency between the data at different locations. Based on the topology of the graph, we sparsify the Transformer to account for the strength of spatial dependency, long-range temporal dependency, data non-stationarity, and data heterogeneity. We evaluate Forecaster in the problem of forecasting taxi ride-hailing demand and show that our proposed architecture significantly outperforms the state-of-the-art baselines.