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Related Experiment Video

Updated: Jul 6, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

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Published on: July 3, 2020

Using meta-learning to recommend an appropriate time-series forecasting model.

Nasrin Talkhi¹, Narges Akhavan Fatemi², Mehdi Jabbari Nooghabi³

¹Department of Biostatistics, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran.

BMC Public Health

|January 10, 2024

Summary

A machine learning approach effectively recommends forecasting models for COVID-19 data. The decision tree model accurately classifies time series, suggesting Auto-Regressive Integrated Moving Average (ARIMA) or exponential smoothing state-space model with Trigonometric seasonality, Box-Cox transformation, ARMA errors, Trend, and Seasonal components (TBATS) for future predictions.

Keywords:

ARIMA COVID-19 Forecasting Machine-learning Meta-learning TBATS

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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Area of Science:

Time series analysis
Machine learning
Epidemiological forecasting

Background:

Selecting appropriate univariate time series forecasting algorithms is challenging due to numerous options.
Expert knowledge for model selection is not always feasible due to resource constraints.

Purpose of the Study:

To develop a meta-learning approach for recommending forecasting models (ARIMA and TBATS) for COVID-19 data.
To evaluate the performance of machine learning algorithms in classifying time series characteristics for model selection.

Main Methods:

Utilized daily COVID-19 confirmed, death, and recovered case data from 187 countries (Feb 2020-May 2021).
Applied Auto-Regressive Integrated Moving Average (ARIMA) and TBATS models for forecasting.
Extracted time series meta-features and employed Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and Artificial Neural Network (ANN) as meta-learners.

Main Results:

The Decision Tree (DT) model demonstrated superior performance in time series classification.
DT achieved 87.50% training accuracy and 82.50% testing accuracy.
DT exhibited high sensitivity and specificity in both training and testing phases.

Conclusions:

The meta-learning approach successfully predicted appropriate forecasting models (ARIMA/TBATS) based on time series features.
The DT model can recommend ARIMA or TBATS for forecasting COVID-19 trends (confirmed, death, recovered cases).