Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

38
Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
38
Distribution Reliability and Automation01:25

Distribution Reliability and Automation

105
Distribution reliability in electrical power systems is critical for ensuring an uninterrupted power supply to consumers at minimal cost. According to IEEE Standard Terms, reliability is the probability that a device will function without failure over a specified time period or amount of usage. For electric power distribution, this translates to maintaining continuous power supply and addressing customer concerns over power outages. Several indices, as defined by IEEE Standard 1366-2012, are...
105
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

86
Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
86
Statistical Analysis: Overview01:11

Statistical Analysis: Overview

6.0K
When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
6.0K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

38
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
38
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

56
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
56

You might also read

Related Articles

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

Sort by
Same author

Permutation Tests Based on the Copula-Graphic Estimator and Their Use for Survival Tree Construction.

Statistics in medicine·2026
Same author

DaRA Dataset: Combining Wearable Sensors, Location Tracking, and Process Knowledge for Enhanced Human Activity and Human Context Recognition in Warehousing.

Sensors (Basel, Switzerland)·2026
Same author

Early and Late Buzzards: Comparing Different Approaches for Quantile-Based Multiple Testing in Heavy-Tailed Wildlife Research Data.

Biometrical journal. Biometrische Zeitschrift·2025
Same author

Incompletely Observed Nonparametric Factorial Designs With Repeated Measurements: A Wild Bootstrap Approach.

Biometrical journal. Biometrische Zeitschrift·2024
Same author

Sample Size Calculation Under Nonproportional Hazards Using Average Hazard Ratios.

Biometrical journal. Biometrische Zeitschrift·2024
Same author

The consequences of neglected confounding and interactions in mixed-effects meta-regression: An illustrative example.

Research synthesis methods·2023
Same journal

Research on a Regional Availability Evaluation Model for Road-Area High-Entropy Energy Based on Synergy Factors.

Entropy (Basel, Switzerland)·2026
Same journal

Atmospheric Turbulence Channel Modeling and Performance Analysis of a CO-ZP-OFDM Coherent Optical Communication System for UAV Air-to-Ground Scenarios.

Entropy (Basel, Switzerland)·2026
Same journal

Information Geometry and Asymptotic Theory for SMML Estimators.

Entropy (Basel, Switzerland)·2026
Same journal

Correlation Entropy and Power-Law Kinetics.

Entropy (Basel, Switzerland)·2026
Same journal

Research on the Contagion of Systemic Financial Risk Under the Impact of Climate Risks-From the Perspective of Complex Networks and Machine Learning.

Entropy (Basel, Switzerland)·2026
Same journal

The Statistical-Mechanical Meaning of the Wave Function of Quantum Mechanics.

Entropy (Basel, Switzerland)·2026
See all related articles
  1. Home
  3. Comparing Statistical And Machine Learning Methods For Time Series Forecasting In Data-driven Logistics-a Simulation Study.
  1. Home
  3. Comparing Statistical And Machine Learning Methods For Time Series Forecasting In Data-driven Logistics-a Simulation Study.

Related Experiment Video

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
07:59

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

Published on: June 9, 2023

1.3K

Comparing Statistical and Machine Learning Methods for Time Series Forecasting in Data-Driven Logistics-A Simulation

Lena Schmid1, Moritz Roidl2, Alice Kirchheim2,3

  • 1Department of Statistics, TU Dortmund University, 44227 Dortmund, Germany.

Entropy (Basel, Switzerland)
|January 24, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

Accurate forecasting is crucial for logistics and supply chain planning. Machine learning, particularly Random Forests, excels in complex scenarios, while time series methods are competitive in low-noise environments.

Keywords:
forecastingmachine learningsimulation studytime series

More Related Videos

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

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

Published on: July 3, 2020

3.3K
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.6K

Related Experiment Videos

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
07:59

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

Published on: June 9, 2023

1.3K
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

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

Published on: July 3, 2020

3.3K
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.6K

Area of Science:

  • Operations Research
  • Supply Chain Management
  • Data Science

Background:

  • Logistics and supply chain management heavily rely on accurate forecasting of time-dependent factors.
  • The quality of planning and decision-making is directly linked to forecast accuracy.
  • Existing research often focuses on case-specific forecasting, limiting generalizability.

Purpose of the Study:

  • To compare the performance of various state-of-the-art forecasting methods.
  • To provide general recommendations for forecasting in logistics and supply chain management.
  • To evaluate methods across a broad set of simulated time series representing diverse scenarios.

Main Methods:

  • Simulation of various linear and nonlinear time series.
  • Comparison of different forecasting techniques, including machine learning and time series approaches.
  • Evaluation of forecasting performance under different noise levels and complexities.

    • Main Results:

    • Machine learning methods, especially Random Forests, demonstrated superior performance in complex scenarios.
    • Differentiated time series training significantly enhanced the robustness of machine learning models.
    • Traditional time series approaches remained competitive in low-noise environments.

    Conclusions:

    • Random Forests offer robust forecasting solutions for complex logistics and supply chain challenges.
    • The choice of forecasting method should consider the complexity and noise level of the time series data.
    • Generalizable recommendations for forecasting can be derived from broad simulation studies.