Concept

Mean percentage error

In statistics, the mean percentage error (MPE) is the computed average of percentage errors by which forecasts of a model differ from actual values of the quantity being forecast. The formula for the mean percentage error is: where at is the actual value of the quantity being forecast, ft is the forecast, and n is the number of different times for which the variable is forecast. Because actual rather than absolute values of the forecast errors are used in the formula, positive and negative forecast errors can offset each other; as a result the formula can be used as a measure of the bias in the forecasts. A disadvantage of this measure is that it is undefined whenever a single actual value is zero.

Official source

https://en.wikipedia.org/wiki/Mean_percentage_error

About this result

This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Related lectures (8)

Real Estate Market Trends

Explores the evolution of real estate prices, hedonic models, and the UBS Swiss Real Estate Bubble Index.

Mean-Square-Error Inference

Covers the concept of mean-square-error inference and optimal estimators for inference problems using different design criteria.

Linear Regression Basics

Covers the basics of linear regression in machine learning, including model training, loss functions, and evaluation metrics.

Related publications (21)

Framework for multi-scale planning of the Swiss hydro-power operations: from forecast to grid integration

Hydro-power is the backbone of the Swiss electricity system, accounting for approximately two-thirds of the country’s electricity production. Cleverly managing these plants is essential to maximizing profit while balancing demand and production at any time. However, that strategy is often compromised by its dependence on uncertain future inflows, electricity prices, and demand. To help operators in the decision-making process and to simulate the storage hydro-power behavior in the grid, we propose a generic

Frontiers Media SA2026

Mobgap: A State-of-the-Art Python Framework for Reproducible Estimation and Algorithm Validation of Digital Mobility Outcomes from a Single Wearable Device

Anisoara Ionescu

Objective, continuous assessment of real-world mobility using wearables has significant potential to transform clinical research and practice, yet the field lacks standardised, open-source tools that enable reproducible algorithm real-world validation, across multiple clinical cohorts. This would improve transparency around definitions and performance, thereby enhancing interpretation and more meaningful comparison across studies. The Mobilise-D consortium validated a comprehensive analytical pipeline for e

MDPI AG2026

Numerical thermal–hydraulic modeling and generalized roughness-function correlations for high-aspect-ratio single-sided ribbed channels

Jürg Alexander Schiffmann

High-aspect-ratio cooling channels with single-sided rib roughness are increasingly used in wall-integrated turbine blade designs; however, generalized thermal–hydraulic scaling frameworks for such asymmetric multi-pass configurations remain limited, particularly for channels combining high aspect ratio, single-wall ribbing, turning regions, and engine-relevant Reynolds numbers. This study develops a validated numerical framework to investigate heat transfer and pressure loss in high-aspect-ratio nominally

2026

Related units (1)

Adaptive Systems Laboratory

Related concepts (1)

Mean absolute error

In statistics, mean absolute error (MAE) is a measure of errors between paired observations expressing the same phenomenon. Examples of Y versus X include comparisons of predicted versus observed, subsequent time versus initial time, and one technique of measurement versus an alternative technique of measurement. MAE is calculated as the sum of absolute errors divided by the sample size: It is thus an arithmetic average of the absolute errors , where is the prediction and the true value.

Graph Chat

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

Official source

https://en.wikipedia.org/wiki/Mean_percentage_error

About this result

Related lectures (8)

Real Estate Market Trends

Explores the evolution of real estate prices, hedonic models, and the UBS Swiss Real Estate Bubble Index.

Mean-Square-Error Inference

Covers the concept of mean-square-error inference and optimal estimators for inference problems using different design criteria.

Linear Regression Basics

Covers the basics of linear regression in machine learning, including model training, loss functions, and evaluation metrics.

Related publications (21)

Framework for multi-scale planning of the Swiss hydro-power operations: from forecast to grid integration

Frontiers Media SA2026

Mobgap: A State-of-the-Art Python Framework for Reproducible Estimation and Algorithm Validation of Digital Mobility Outcomes from a Single Wearable Device

Anisoara Ionescu

MDPI AG2026

Numerical thermal–hydraulic modeling and generalized roughness-function correlations for high-aspect-ratio single-sided ribbed channels

Jürg Alexander Schiffmann

2026

Related people (9)

Arash Amini

Pedram Pad

Michaël Unser

Expertise Image Processing, Medical Imaging, Biological Imaging, Wavelets, Splines, Multiresolution Michael Unser is Full Professor with EPFL’s school of Engineering and the academic director of EPFL's Center for Imaging, Lausanne, Switzerland. His primary areas of investigation are signal processing, biomedical imaging and applied functional analysis. He is internationally recognized for his research contributions to sampling theory, wavelets, the use of splines for image processing, stochastic processes,

Nicolas Macris

Nicolas Macris received the PhD degree in theoretical physics from EPFL and then pursued his scientific activity at the mathematics department of Rutgers University (NJ, USA). He then joined the Faculty of Basic Science of EPFL, working in the field of quantum statistical mechanics and mathematical aspects of the quantum Hall effect. Since 2005 he is with the Communication Theories Laboratory and Information Processing group of the School of Communication and Computer Science and currently works at the interface of statistical mechanics, information theory and error correcting codes, inference and learning theory. He held long-term visiting appointments and collaborations with the University College and the Institute of Advanced studies in Dublin, the Ecole Normale Supérieure de Lyon, the Centre de Physique Theorique Luminy Marseille, Paris XI Orsay, the ETH Zürich and more recently Los Alamos National Lab. CV and publication list.

Jérôme Chenal

Expertise Urban DesignUrban PlanningUrban ManagementPublic SpaceHousingDevelopment StudiesVisual Anthropology Mission Director of CEAT Director of Excellence in Africa Center Affiliate professor at UM6P Teaching & PhD Past EPFL PhD Students Stéphanie Hasler, Rémi Jaligot, Armel Firmin Kemajou Mbianda, Martí Bosch Padrós, Andrea Salmi, Vitor Pessoa Colombo, Akuto Akpedze Konou, Rim Mrani Past EPFL PhD Students as codirector Marc Antoine Messer, Alexandre Rigal Courses Urban planning in the South PE

Francesca Rodino

Expertise Electrochemical BiosensorsSensors and Biosensors interfaces | front-end electronicsBiomedical circuits and systems Francesca Rodino received the B.Sc. and M.Sc. degrees in Biomedical Engineering from the Politecnico di Torino, Turin, Italy, in 2019 and 2022, respectively. She developed her Master's project in the field of cortical neuroprosthesis devices for artificial vision in collaboration with the Integrated Circuits Laboratory (ICLAB) at école Polytechnique Fédérale de Lausanne (EPFL) in Swit

Lin Du

Lin Du obtained her BSc in Electronic Information Engineering and her MSc in Information and Communication Engineering respectively from Beijing Institute of Technology, China.From December 2022, she joined Integrated Circuits Laboratory (ICLAB) at EPFL. She is studying for her PhD in Electrical Engineering focusing on machine learning algorithms for drug concentration estimation from data acquired by the sensors. The PhD project is co-supervised by Prof. Sandro Carrara (EPFL) and Prof. Yann Thoma (HEIG-VD)

Michael Christoph Gastpar

Michael Gastpar is a (full) Professor at EPFL. From 2003 to 2011, he was a professor at the University of California at Berkeley, earning his tenure in 2008. He received his Dipl. El.-Ing. degree from ETH Zürich, Switzerland, in 1997 and his MS degree from the University of Illinois at Urbana-Champaign, IL, USA, in 1999. He defended his doctoral thesis at EPFL on Santa Claus day, 2002. He was also a (full) Professor at Delft University of Technology, The Netherlands. His research interests are in network information theory and related coding and signal processing techniques, with applications to sensor networks and neuroscience. He is a Fellow of the IEEE. He is the co-recipient of the 2013 Communications Society & Information Theory Society Joint Paper Award. He was an Information Theory Society Distinguished Lecturer (2009-2011). He won an ERC Starting Grant in 2010, an Okawa Foundation Research Grant in 2008, an NSF CAREER award in 2004, and the 2002 EPFL Best Thesis Award. He has served as an Associate Editor for Shannon Theory for the IEEE Transactions on Information Theory (2008-11), and as Technical Program Committee Co-Chair for the 2010 International Symposium on Information Theory, Austin, TX.

Pakshal Narendra Bohra

Related units (1)

Adaptive Systems Laboratory

Related concepts (1)

Related people (9)

Michael Christoph Gastpar

Pakshal Narendra Bohra