Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Time-Series Graph

Time-Series Graph

A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...

End Point Prediction: Gran Plot

End Point Prediction: Gran Plot

A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Mortality trends and disparities for coexisting heart failure and thyroid disease: A retrospective analysis of deaths in the United States from 1999 to 2023.

Medicine·2026

Same author

Center-specific Federated Learning for Radiation Pneumonitis:A Cross-Center Adaptive Alternating Framework.

International journal of radiation oncology, biology, physics·2026

Same author

Reactor performance and microbial responses of sulfate-reducing bacteria sludge under stepwise polyvinyl chloride microplastic exposure.

Bioresource technology·2026

Same author

Association of hormone receptor-based pathological features with prognosis in high-grade serous ovarian carcinoma: a retrospective cohort study and nomogram construction.

Journal of ovarian research·2026

Same author

OsMAPK20-1 and OsMAPK20-4 phosphorylate OsCK2β3 to regulate its stability in response to phosphate starvation in rice.

Plant physiology·2026

Same author

Photocatalyst-free EDA-complex-enabled radical difluoroalkylation/cyclization for access to difluoroalkylated polycyclic coumarin derivatives.

Chemical communications (Cambridge, England)·2026

Search research articles

Related Experiment Video

Updated: Sep 19, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

AMDCnet: attention-gate-based multi-scale decomposition and collaboration network for long-term time series

Shikang Hou¹, Song Sun², Tao Yin¹

¹School of Big Data and Software Engineering, Chongqing University, Chongqing, China.

Frontiers in Artificial Intelligence

|June 16, 2025

Summary

This summary is machine-generated.

AMDCnet, a novel network, effectively models multi-scale time series by decomposing and integrating data across various temporal resolutions, outperforming traditional methods in forecasting tasks.

Keywords:

attention-gate feature fusion forecasting long-term time series multi-scale decomposition

More Related Videos

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

Published on: August 5, 2014

Related Experiment Videos

Last Updated: Sep 19, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

Published on: August 5, 2014

Area of Science:

Artificial Intelligence
Data Science
Machine Learning

Background:

Traditional time series analysis often neglects dependencies across multiple temporal scales.
Extracting features from multi-scale time series data presents challenges due to complex structures.
Existing methods may not fully capture intricate temporal dynamics at different resolutions.

Purpose of the Study:

To introduce AMDCnet, a network designed for multi-scale time series decomposition and collaboration.
To enhance the modeling capacity for decomposing and integrating data across varying time scales.
To improve feature extraction and fusion for complex time series data.

Main Methods:

AMDCnet transforms time series into multiple temporal resolutions for multi-scale feature decomposition.
It preserves overall temporal dynamics while extracting features from downsampled sequences.
Attention-gated co-training mechanisms integrate multi-resolution features.

Main Results:

AMDCnet achieved 44 best and 10 second-best results across 64 evaluated cases.
Demonstrated state-of-the-art performance in time series forecasting on 8 benchmark datasets.
Effectively captured dependencies across different temporal scales in multivariate time series.

Conclusions:

AMDCnet offers a robust baseline for AI in multivariate time series forecasting.
The approach leverages multi-scale decomposition and gated units for effective feature fusion.
Future work can optimize scale decomposition and fusion for enhanced multi-scale information representation.