Long sequence time-series forecasting with deep learning: A survey
The development of deep learning technology has brought great improvements to the field
of time series forecasting. Short sequence time-series forecasting no longer satisfies the …
of time series forecasting. Short sequence time-series forecasting no longer satisfies the …
MCNS: Mining Causal Natural Structures Inside Time Series via A Novel Internal Causality Scheme
Y Liu, D Du, Z Jiang, A Huang, Y Li - arXiv preprint arXiv:2309.06739, 2023 - arxiv.org
Causal inference permits us to discover covert relationships of various variables in time
series. However, in most existing works, the variables mentioned above are the dimensions …
series. However, in most existing works, the variables mentioned above are the dimensions …
A Knowledge Graph for Query-Induced Analyses of Hierarchically Structured Time Series Information
This paper introduces the concept of a knowledge graph for time series data, which allows
for a structured management and propagation of characteristic time series information and …
for a structured management and propagation of characteristic time series information and …
[PDF][PDF] Representation learning of time series data with high-level semantic
Y Chengyang, M Qiang - DEIM22, 2022 - proceedings-of-deim.github.io
This study proposes a representation method of time-series data by considering its high-
level semantic features to help build interpretable neural networks. This method constructs …
level semantic features to help build interpretable neural networks. This method constructs …