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Multi-scale adaptive graph neural network for multivariate time series forecasting
Multivariate time series (MTS) forecasting plays an important role in the automation and
optimization of intelligent applications. It is a challenging task, as we need to consider both …
optimization of intelligent applications. It is a challenging task, as we need to consider both …
Deep graph gated recurrent unit network-based spatial–temporal multi-task learning for intelligent information fusion of multiple sites with application in short-term …
Accurate photovoltaic (PV) power forecast is crucial for carbon neutrality. Current researches
on PV power forecast mainly focus on using temporal information from single PV station, and …
on PV power forecast mainly focus on using temporal information from single PV station, and …
MPGE and RootRank: A sufficient root cause characterization and quantification framework for industrial process faults
Root cause diagnosis can locate abnormalities of industrial processes, ensuring production
safety and manufacturing efficiency. However, existing root cause diagnosis models only …
safety and manufacturing efficiency. However, existing root cause diagnosis models only …
[HTML][HTML] Exploring the effect of climate risk on agricultural and food stock prices: Fresh evidence from EMD-Based variable-lag transfer entropy analysis
Climate has traditionally played an important role in the development of countries, owing to
its inherent relationship with agricultural output and pricing. This study explores one such …
its inherent relationship with agricultural output and pricing. This study explores one such …
Auto iv: Counterfactual prediction via automatic instrumental variable decomposition
Instrumental variables (IVs), sources of treatment randomization that are conditionally
independent of the outcome, play an important role in causal inference with unobserved …
independent of the outcome, play an important role in causal inference with unobserved …
Quantifying information transfer among clean energy, carbon, oil, and precious metals: A novel transfer entropy-based approach
Measuring the strength and direction of information flow between markets plays a vital role
for investors and policymakers. In this study, we propose a novel approach: the empirical …
for investors and policymakers. In this study, we propose a novel approach: the empirical …
Causal structure learning for high-dimensional non-stationary time series
S Chen, HT Wu, G ** - Knowledge-Based Systems, 2024 - Elsevier
Learning the causal structure of high-dimensional non-stationary time series can help in
understanding the data generation mechanism, which is a crucial task in machine learning …
understanding the data generation mechanism, which is a crucial task in machine learning …
An Extensive Survey with Empirical Studies on Deep Temporal Point Process
Temporal point process as the stochastic process on a continuous domain of time is
commonly used to model the asynchronous event sequence featuring occurrence …
commonly used to model the asynchronous event sequence featuring occurrence …
Sequential recommendation via an adaptive cross-domain knowledge decomposition
Cross-domain recommendation, as an intelligent machine to alleviate data sparsity and cold
start problems, has attracted extensive attention from scholars. Existing cross-domain …
start problems, has attracted extensive attention from scholars. Existing cross-domain …
Accurate four-hour-ahead probabilistic forecast of photovoltaic power generation based on multiple meteorological variables-aided intelligent optimization of numeric …
Accurate four-hour-ahead PV power prediction is crucial to the utilization of PV power.
Conventional methods focus on using historical data directly. This paper addresses this …
Conventional methods focus on using historical data directly. This paper addresses this …