# Joint Modeling of Local and Global Temporal Dynamics for Multivariate Time Series Forecasting with Missing Values

arXiv: https://arxiv.org/abs/1911.10273 · HTML: https://mlsanity.com/paper/1911.10273/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Nov 2019.

## Built on by (most cited first, 30 of 37)

- [Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects](https://mlsanity.com/paper/2306.10125/) (2023)
- [STDEN: Towards Physics-Guided Neural Networks for Traffic Flow Prediction](https://mlsanity.com/paper/2209.00225/) (2022)
- [GT-GAN: General Purpose Time Series Synthesis with Generative Adversarial Networks](https://mlsanity.com/paper/2210.02040/) (2022)
- [Dynamic Gaussian Mixture based Deep Generative Model For Robust Forecasting on Sparse Multivariate Time Series](https://mlsanity.com/paper/2103.02164/) (2021)
- [Towards Generating Real-World Time Series Data](https://mlsanity.com/paper/2111.08386/) (2021)
- [A Multi-view Multi-task Learning Framework for Multi-variate Time Series Forecasting](https://mlsanity.com/paper/2109.01657/) (2021)
- [GinAR: An End-To-End Multivariate Time Series Forecasting Model Suitable for Variable Missing](https://mlsanity.com/paper/2405.11333/) (2024)
- [Merlin: Multi-View Representation Learning for Robust Multivariate Time Series Forecasting with Unfixed Missing Rates](https://mlsanity.com/paper/2506.12459/) (2025)
- [Event-Aware Multimodal Mobility Nowcasting](https://mlsanity.com/paper/2112.08443/) (2021)
- [Time Series Continuous Modeling for Imputation and Forecasting with Implicit Neural Representations](https://mlsanity.com/paper/2306.05880/) (2023)
- [Graph-based Forecasting with Missing Data through Spatiotemporal Downsampling](https://mlsanity.com/paper/2402.10634/) (2024)
- [HP-GMN: Graph Memory Networks for Heterophilous Graphs](https://mlsanity.com/paper/2210.08195/) (2022)
- [TimeCHEAT: A Channel Harmony Strategy for Irregularly Sampled Multivariate Time Series Analysis](https://mlsanity.com/paper/2412.12886/) (2024)
- [Graph Convolutional Networks for Traffic Forecasting with Missing Values](https://mlsanity.com/paper/2212.06419/) (2022)
- [LITE: Modeling Environmental Ecosystems with Multimodal Large Language Models](https://mlsanity.com/paper/2404.01165/) (2024)
- [Revisiting Multivariate Time Series Forecasting with Missing Values](https://mlsanity.com/paper/2509.23494/) (2025)
- [S4M: S4 for multivariate time series forecasting with Missing values](https://mlsanity.com/paper/2503.00900/) (2025)
- [LIFE: Learning Individual Features for Multivariate Time Series Prediction with Missing Values](https://mlsanity.com/paper/2109.14844/) (2021)
- [Multi-Variate Time Series Forecasting on Variable Subsets](https://mlsanity.com/paper/2206.12626/) (2022)
- [Learning to Estimate Package Delivery Time in Mixed Imbalanced Delivery and Pickup Logistics Services](https://mlsanity.com/paper/2505.00375/) (2025)
- [Considering Nonstationary within Multivariate Time Series with Variational Hierarchical Transformer for Forecasting](https://mlsanity.com/paper/2403.05406/) (2024)
- [NuwaTS: a Foundation Model Mending Every Incomplete Time Series](https://mlsanity.com/paper/2405.15317/) (2024)
- [DeepSTA: A Spatial-Temporal Attention Network for Logistics Delivery Timely Rate Prediction in Anomaly Conditions](https://mlsanity.com/paper/2505.00402/) (2025)
- [Global Convolutional Neural Processes](https://mlsanity.com/paper/2109.00691/) (2021)
- [LogoRA: Local-Global Representation Alignment for Robust Time Series Classification](https://mlsanity.com/paper/2409.12169/) (2024)
- [Learning Continuous Network Emerging Dynamics from Scarce Observations via Data-Adaptive Stochastic Processes](https://mlsanity.com/paper/2310.16466/) (2023)
- [DBT-DMAE: An Effective Multivariate Time Series Pre-Train Model under Missing Data](https://mlsanity.com/paper/2209.07798/) (2022)
- [Inductive Spatial Temporal Prediction Under Data Drift with Informative Graph Neural Network](https://mlsanity.com/paper/2409.13253/) (2024)
- [IBN: An Interpretable Bidirectional-Modeling Network for Multivariate Time Series Forecasting with Variable Missing](https://mlsanity.com/paper/2509.07725/) (2025)
- [Beyond Observations: Reconstruction Error-Guided Irregularly Sampled Time Series Representation Learning](https://mlsanity.com/paper/2511.06854/) (2025)

## Abstract (arXiv)

Multivariate time series (MTS) forecasting is widely used in various domains, such as meteorology and traffic. Due to limitations on data collection, transmission, and storage, real-world MTS data usually contains missing values, making it infeasible to apply existing MTS forecasting models such as linear regression and recurrent neural networks. Though many efforts have been devoted to this problem, most of them solely rely on local dependencies for imputing missing values, which ignores global temporal dynamics. Local dependencies/patterns would become less useful when the missing ratio is high, or the data have consecutive missing values; while exploring global patterns can alleviate such problems. Thus, jointly modeling local and global temporal dynamics is very promising for MTS forecasting with missing values. However, work in this direction is rather limited. Therefore, we study a novel problem of MTS forecasting with missing values by jointly exploring local and global temporal dynamics. We propose a new framework LGnet, which leverages memory network to explore global patterns given estimations from local perspectives. We further introduce adversarial training to enhance the modeling of global temporal distribution. Experimental results on real-world datasets show the effectiveness of LGnet for MTS forecasting with missing values and its robustness under various missing ratios.