# Towards Invariant Time Series Forecasting in Smart Cities

arXiv: https://arxiv.org/abs/2405.05430 · HTML: https://mlsanity.com/paper/2405.05430/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 15 references that are in the map.
- First posted on arXiv 8 May 2024.

## Builds on (most cited first, 4 of 4)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Invariant Risk Minimization](https://mlsanity.com/paper/1907.02893/) (2019)
- [Adapting Convolutional Neural Networks for Geographical Domain Shift](https://mlsanity.com/paper/1901.06345/) (2019)

## Built on by (most cited first, 6 of 6)

- [Out-of-Distribution Generalization in Time Series: A Survey](https://mlsanity.com/paper/2503.13868/) (2025)
- [Predict Click-Through Rates with Deep Interest Network Model in E-commerce Advertising](https://mlsanity.com/paper/2406.10239/) (2024)
- [Learning Flexible Time-windowed Granger Causality Integrating Heterogeneous Interventional Time Series Data](https://mlsanity.com/paper/2406.10419/) (2024)
- [Bi-level Heterogeneous Learning for Time Series Foundation Models: A Federated Learning Approach](https://mlsanity.com/paper/2604.06727/) (2026)
- [Wormhole: Concept-Aware Deep Representation Learning for Co-Evolving Sequences](https://mlsanity.com/paper/2409.13857/) (2024)
- [MuST2-Learn: Multi-view Spatial-Temporal-Type Learning for Heterogeneous Municipal Service Time Estimation](https://mlsanity.com/paper/2508.16503/) (2025)

## Abstract (arXiv)

In the transformative landscape of smart cities, the integration of the cutting-edge web technologies into time series forecasting presents a pivotal opportunity to enhance urban planning, sustainability, and economic growth. The advancement of deep neural networks has significantly improved forecasting performance. However, a notable challenge lies in the ability of these models to generalize well to out-of-distribution (OOD) time series data. The inherent spatial heterogeneity and domain shifts across urban environments create hurdles that prevent models from adapting and performing effectively in new urban environments. To tackle this problem, we propose a solution to derive invariant representations for more robust predictions under different urban environments instead of relying on spurious correlation across urban environments for better generalizability. Through extensive experiments on both synthetic and real-world data, we demonstrate that our proposed method outperforms traditional time series forecasting models when tackling domain shifts in changing urban environments. The effectiveness and robustness of our method can be extended to diverse fields including climate modeling, urban planning, and smart city resource management.