Search Results for author:
Found 8 papers, 3 papers with code
PORCA: Root Cause Analysis with Partially Observed Data
In this paper, we unveil the issues of unobserved confounders and heterogeneity in partial observation and come up with a new problem of root cause analysis with partially observed data.
STBench: Assessing the Ability of Large Language Models in Spatio-Temporal Analysis
The rapid evolution of large language models (LLMs) holds promise for reforming the methodology of spatio-temporal data mining.
CausalMMM: Learning Causal Structure for Marketing Mix Modeling
We argue that causal MMM needs dynamically discover specific causal structures for different shops and the predictions should comply with the prior known marketing response patterns.
AnomalyLLM: Few-shot Anomaly Edge Detection for Dynamic Graphs using Large Language Models
With the evolving of time, the types of anomaly edges are emerging and the labeled anomaly samples are few for each type.
Exploring Progress in Multivariate Time Series Forecasting: Comprehensive Benchmarking and Heterogeneity Analysis
Moreover, based on the proposed BasicTS and rich heterogeneous MTS datasets, we conduct an exhaustive and reproducible performance and efficiency comparison of popular models, providing insights for researchers in selecting and designing MTS forecasting models.
Causal Discovery from Temporal Data: An Overview and New Perspectives
Existing causal discovery works can be divided into two highly correlated categories according to whether the temporal data is calibrated, ie, multivariate time series causal discovery, and event sequence causal discovery.
HTGN-BTW: Heterogeneous Temporal Graph Network with Bi-Time-Window Training Strategy for Temporal Link Prediction
The Temporal Link Prediction task of WSDM Cup 2022 expects a single model that can work well on two kinds of temporal graphs simultaneously, which have quite different characteristics and data properties, to predict whether a link of a given type will occur between two given nodes within a given time span.
CausalMTA: Eliminating the User Confounding Bias for Causal Multi-touch Attribution
Existing methods first train a model to predict the conversion probability of the advertisement journeys with historical data and calculate the attribution of each touchpoint using counterfactual predictions.
