2021
DOI: 10.1109/lra.2021.3095907
|View full text |Cite
|
Sign up to set email alerts
|

Industrial Time Series Modeling With Causal Precursors and Separable Temporal Convolutions

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1

Citation Types

0
2
0

Year Published

2022
2022
2024
2024

Publication Types

Select...
4
2

Relationship

1
5

Authors

Journals

citations
Cited by 8 publications
(2 citation statements)
references
References 23 publications
0
2
0
Order By: Relevance
“…For instance, Wang et al [28] used TCN and LightGBM for electrical load predictions, and feature extraction of multiple long-term sequences was performed by TCN. Menegozzo et al [29] used an improved TCN to enhance the feature extraction capability for food production prediction. In this study, we take advantage of the excellent feature extraction capability of TCN to facilitate model building.…”
Section: Related Workmentioning
confidence: 99%
“…For instance, Wang et al [28] used TCN and LightGBM for electrical load predictions, and feature extraction of multiple long-term sequences was performed by TCN. Menegozzo et al [29] used an improved TCN to enhance the feature extraction capability for food production prediction. In this study, we take advantage of the excellent feature extraction capability of TCN to facilitate model building.…”
Section: Related Workmentioning
confidence: 99%
“…SCM is not limited in recognizing patterns but allows to reason about the underlying structure beyond the observed correlation, such as simulating interventions with do-calculus or assuming hypothetical changes in the environments with counterfactuals [6]. Thus, several efforts have been made to integrate associative methods within SCM [7]. In particular, a common goal is to recognize the causal relationships, also referred as Causal Discovery (CD), from data and build a causal model from the observed system's behavior.…”
Section: Introductionmentioning
confidence: 99%