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

Steps in Outbreak Investigation

Steps in Outbreak Investigation

In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:

Discrete-Time Fourier Series

Discrete-Time Fourier Series

The Discrete-Time Fourier Series (DTFS) is a fundamental concept in signal processing, serving as the discrete-time counterpart to the continuous-time Fourier series. It allows for the representation and analysis of discrete-time periodic signals in terms of their frequency components. Unlike its continuous counterpart, which utilizes integrals, the calculation of DTFS expansion coefficients involves summations due to the discrete nature of the signal.
For a discrete-time periodic signal x[n]...

You might also read

Related Articles

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

Sort by

Same author

Trivalent Gadolinium Ions Forming Injectable Hydrogels for Simultaneous In Situ Vaccination Therapy and Imaging of Soft Tissue Sarcoma.

Advanced healthcare materials·2026

Same author

Associations of lung function abnormalities with the onset and progression of mental disorders in the UK Biobank.

Journal of affective disorders·2026

Same author

Blocking SHP2 benefits FGFR2 inhibitor and overcomes its resistance in <i>FGFR2</i>-amplified gastric cancer.

eLife·2026

Same author

Unveiling the hidden genetic conundrum linking pulmonary diseases and neuropsychiatric disorders: Novel insights from advanced omics-based analyses of the lung-brain axis.

Neurobiology of disease·2026

Same author

Discoveries in non-symbiotic environments: Dynamic changes and potential contributions of arbuscular mycorrhizal fungi in cigar tobacco fermentation.

Current research in microbial sciences·2026

Same author

A hierarchical honeycomb-structured Pd/MgO/Mg catalyst <i>via</i> microplasma electrolytic oxidation and cathodic etching for efficient green oxidation of hydrosilanes.

Chemical communications (Cambridge, England)·2026

Same journal

Q-learning based asynchronous Boolean control networks stabilization with data loss.

Neural networks : the official journal of the International Neural Network Society·2026

Same journal

New results on prescribed-time synchronization of complex networks via intermittent control.

Neural networks : the official journal of the International Neural Network Society·2026

Same journal

Variance-constrained multi-view ensemble broad network for imbalanced data.

Neural networks : the official journal of the International Neural Network Society·2026

Same journal

Dynamic analysis and reliable mechanical optimization application of ring HNN effected with a memristive neuron.

Neural networks : the official journal of the International Neural Network Society·2026

Same journal

DAFF-Net: A detection and search method for small-scale low surface brightness galaxies.

Neural networks : the official journal of the International Neural Network Society·2026

Same journal

Quasi-synchronization for complex networks with hybrid pinning intermittent control.

Neural networks : the official journal of the International Neural Network Society·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jan 9, 2026

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

Iterative feedback-based time-series anomaly detection with adaptive diffusion models.

Chunjing Xiao¹, Xianghe Du¹, Xueru Song¹

¹Henan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng, 475004, China.

Neural Networks : the Official Journal of the International Neural Network Society

|December 6, 2025

Summary

This summary is machine-generated.

This study introduces an Iterative Feedback-based Anomaly Detection (IFAD) framework to improve time series anomaly detection. IFAD enhances accuracy by adaptively selecting normal points and smoothing data, outperforming existing methods.

Keywords:

Anomaly detection Data imputation Diffusion models Time series

More Related Videos

Age-dependent Dynamics of Locomotion in Caenorhabditis elegans: A Lyapunov Exponent Analysis

Age-dependent Dynamics of Locomotion in Caenorhabditis elegans: A Lyapunov Exponent Analysis

Published on: September 23, 2025

Related Experiment Videos

Last Updated: Jan 9, 2026

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

Age-dependent Dynamics of Locomotion in Caenorhabditis elegans: A Lyapunov Exponent Analysis

Age-dependent Dynamics of Locomotion in Caenorhabditis elegans: A Lyapunov Exponent Analysis

Published on: September 23, 2025

Area of Science:

Computer Science
Data Science
Artificial Intelligence

Background:

Anomaly detection in time series data is critical for many applications.
Current imputation-based methods using diffusion models struggle with user dependency and data distortion.
Existing techniques require significant user expertise for effective anomaly detection.

Purpose of the Study:

To propose a novel Iterative Feedback-based Anomaly Detection (IFAD) framework.
To overcome limitations of user-dependent imputation and data distortion in anomaly detection.
To enhance the performance of anomaly detection in time series data.

Main Methods:

Developed an iterative feedback-based point selection scheme to identify normal points without user input.
Introduced an adaptive conditional diffusion model with dynamic weight-based data smoothing.
Implemented a strategy to adjust the importance of observed points for improved data smoothing.

Main Results:

IFAD demonstrates significant improvements over current state-of-the-art anomaly detection methods.
The framework effectively identifies normal points iteratively, reducing reliance on user expertise.
Adaptive diffusion and data smoothing enhance the accuracy of anomaly detection.

Conclusions:

The proposed IFAD framework offers a robust and user-independent approach to time series anomaly detection.
IFAD enhances detection performance through adaptive point selection and data smoothing.
This method represents a significant advancement in the field of anomaly detection.