[HTML][HTML] Forecasting: theory and practice
Forecasting has always been at the forefront of decision making and planning. The
uncertainty that surrounds the future is both exciting and challenging, with individuals and …
uncertainty that surrounds the future is both exciting and challenging, with individuals and …
Generative time series forecasting with diffusion, denoise, and disentanglement
Time series forecasting has been a widely explored task of great importance in many
applications. However, it is common that real-world time series data are recorded in a short …
applications. However, it is common that real-world time series data are recorded in a short …
Future Trends for Human‐AI Collaboration: A Comprehensive Taxonomy of AI/AGI Using Multiple Intelligences and Learning Styles
A Cichocki, AP Kuleshov - Computational Intelligence and …, 2021 - Wiley Online Library
This article discusses some trends and concepts in developing a new generation of future
Artificial General Intelligence (AGI) systems which relate to complex facets and different …
Artificial General Intelligence (AGI) systems which relate to complex facets and different …
Generative semi-supervised learning for multivariate time series imputation
The missing values, widely existed in multivariate time series data, hinder the effective data
analysis. Existing time series imputation methods do not make full use of the label …
analysis. Existing time series imputation methods do not make full use of the label …
Probabilistic time series forecasting with deep non‐linear state space models
H Du, S Du, W Li - CAAI Transactions on Intelligence …, 2023 - Wiley Online Library
Probabilistic time series forecasting aims at estimating future probabilistic distributions
based on given time series observations. It is a widespread challenge in various tasks, such …
based on given time series observations. It is a widespread challenge in various tasks, such …
Fast autoregressive tensor decomposition for online real-time traffic flow prediction
Online real-time traffic flow prediction typically offers better real-time performance than
offline prediction. However, existing studies rarely discussed online real-time traffic flow …
offline prediction. However, existing studies rarely discussed online real-time traffic flow …
A truncated SVD-based ARIMA model for multiple QoS prediction in mobile edge computing
C Yan, Y Zhang, W Zhong, C Zhang… - Tsinghua Science and …, 2021 - ieeexplore.ieee.org
In the mobile edge computing environments, Quality of Service (QoS) prediction plays a
crucial role in web service recommendation. Because of distinct features of mobile edge …
crucial role in web service recommendation. Because of distinct features of mobile edge …
Tensor representation-based transferability analytics and selective transfer learning of prognostic knowledge for remaining useful life prediction across machines
In recent years, deep transfer learning techniques have been successfully applied to solve
RUL prediction across different working conditions. However, for RUL prediction across …
RUL prediction across different working conditions. However, for RUL prediction across …
METRO: a generic graph neural network framework for multivariate time series forecasting
Multivariate time series forecasting has been drawing increasing attention due to its
prevalent applications. It has been commonly assumed that leveraging latent dependencies …
prevalent applications. It has been commonly assumed that leveraging latent dependencies …
Self-supervised deep tensor domain-adversarial regression adaptation for online remaining useful life prediction across machines
With deep transfer learning techniques, this article focuses on the online remaining useful
life (RUL) prediction problem across different machines and tries to address the following …
life (RUL) prediction problem across different machines and tries to address the following …