Summary of Robust Multivariate Time Series Forecasting Against Intra- and Inter-series Transitional Shift, by Hui He et al.
Robust Multivariate Time Series Forecasting against Intra- and Inter-Series Transitional Shift
by Hui He, Qi Zhang, Kun Yi, Xiaojun Xue, Shoujin Wang, Liang Hu, Longbing Cao
First submitted to arxiv on: 18 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a unified Probabilistic Graphical Model (PGM) to jointly capture intra-series correlations, inter-series correlations, and time-variant transitional distribution for Multivariate Time Series (MTS) forecasting. The proposed model, JointPGM, employs multiple Fourier basis functions to learn dynamic time factors and designs two distinct learners: intra-series learner and inter-series learner. The intra-series learner captures temporal dynamics using temporal gates, while the inter-series learner models spatial dynamics through multi-hop propagation, incorporating Gumbel-softmax sampling. These two types of series dynamics are fused into a latent variable, which is used to infer time factors, generate final predictions, and perform reconstruction. The paper validates the effectiveness and efficiency of JointPGM on six non-stationary MTS datasets, achieving state-of-the-art forecasting performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper develops a new way to forecast data that changes over time. It’s called Multivariate Time Series (MTS) data, which is important because many real-world data sets change in complex ways. The current methods for handling this type of data don’t fully capture the relationships between different parts of the data, and they also ignore what causes these changes to happen. To solve this problem, the paper proposes a new model called JointPGM that can handle both the internal relationships within each part of the data and the external relationships between different parts. This model uses special mathematical techniques to learn about these relationships and then makes predictions based on them. The results show that JointPGM performs better than other methods for forecasting MTS data. |
Keywords
» Artificial intelligence » Softmax » Time series
