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

Search research articles

Related Experiment Video

Updated: Jan 7, 2026

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

A new automatic forecasting method based on explainable deep dendritic artificial neural network.

Eren Bas¹, Erol Egrioglu²

¹Faculty of Arts and Science, Department of Data Science and Analytics, Giresun University, Giresun, Turkey.

Scientific Reports

|December 30, 2025

Summary

This summary is machine-generated.

Related Concept Videos

Neural Circuits

Neural Circuits

Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...

You might also read

Related Articles

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

Sort by

Same author

Pythagorean fuzzy time series approach based on hybrid high-order artificial neural network for nonlinear time series forecasting.

Scientific reports·2026

Same author

A novel approach for forecasting algal bloom: long short-term memory artificial neural network with exponential smoothing feedback optimized by the arithmetic mean algorithm.

Scientific reports·2026

Same author

Machine learning-based integration and comparison of ADC map radiomics with conventional imaging markers for cholesteatoma diagnosis.

Neuroradiology·2026

Same author

A Sugeno-multiplicative neuron hybrid system trained by differential evolution algorithm for time series forecasting.

Scientific reports·2026

Same author

Forecasting Nasdaq stock exchange time series using an improved recurrent spiking Pi-Sigma artificial neural network.

Scientific reports·2026

Same author

Does Margin Length Predict Recurrence After Partial Nephrectomy, or Is Presence Alone Sufficient?

Journal of kidney cancer and VHL·2026

Same journal

Serum vitamin D level and its association with vertigo frequency and severity in Meniere disease.

Scientific reports·2026

Same journal

PFA-Net: a physics-informed feature enhancement and attention network for interpretable bearing fault diagnosis under strong noise.

Scientific reports·2026

Same journal

Circulating inflammatory, redox, and apoptosis-related alterations in drug-naive idiopathic pulmonary fibrosis: an exploratory case-control study.

Scientific reports·2026

Same journal

A baseline-oriented dynamic aggregation approach for demand-side heterogeneous controllable resources.

Scientific reports·2026

Same journal

Temporal precision and accuracy in schizophrenia: an exploratory study.

Scientific reports·2026

Same journal

Prefrontal EEG spectral and nonlinear signatures of subthreshold depression during resting state and affectively valenced picture/video viewing: a participant-level analysis.

Scientific reports·2026

See all related articles

This study introduces automated tests for deep dendritic recurrent neural networks, enhancing forecasting accuracy. A novel automated method based on these tests improves forecasting performance for time series data.

Area of Science:

Artificial Intelligence
Machine Learning
Time Series Analysis

Background:

Automated forecasting methods are crucial for practitioners, reducing subjective decisions in model selection and data preprocessing.
Existing automated methods often rely on complex model/variable selection and hypothesis testing.
Explainability in deep learning models for forecasting remains a significant challenge.

Purpose of the Study:

To propose input significance and model validity tests for deep dendritic recurrent neural networks (DDRNNs) within an explainability framework.
To develop a novel automated forecasting method for DDRNNs leveraging these proposed tests.
To evaluate the forecasting performance of the new method against established techniques using benchmark datasets.

Main Methods:

Keywords:

Automatic forecasting methods Deep neural networks Differential evolution algorithm Forecasting Recurrent neural networks

Related Experiment Videos

Last Updated: Jan 7, 2026

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

Development of statistical tests to assess the significance of inputs to DDRNNs.
Implementation of model validity tests to ensure the reliability of DDRNN forecasts.
Creation of an automated forecasting pipeline for DDRNNs incorporating the developed tests.
Comparative analysis using M3 and M4 competition time series datasets.

Main Results:

The proposed input significance and model validity tests demonstrate effectiveness in evaluating DDRNN components.
The novel automated forecasting method shows competitive or superior performance compared to existing methods on M3 and M4 datasets.
The developed tests contribute to the explainability of DDRNNs in time series forecasting.

Conclusions:

The proposed tests enhance the interpretability and reliability of deep dendritic recurrent neural networks for forecasting.
The new automated method offers a robust and data-driven approach to time series forecasting with DDRNNs.
This work advances the field of automated forecasting by integrating explainability into deep learning models.