SZodixStudyAcademic Library

Shelves Open access

Study · an open academic library·Metadata from OpenAlex (CC0)·Shelves·OpenAlex metadata, de-duplicated

Are forecasting competitions data representative of the reality? · Study

ArticleShelves

ArticleAre forecasting competitions data representative of the reality?

Evangelos Spiliotis2019

Article2019

Are forecasting competitions data representative of the reality?

Evangelos Spiliotis · Andreas Kouloumos · Vassilios Assimakopoulos · Spyros Makridakis

67 citations·0/0 files stored·International Journal of Forecasting

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Forecasting Techniques and Applications Time Series Analysis and Forecasting Traffic Prediction and Management Techniques

Abstract

No abstract was published for this record.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 67 citations exist upstream.

References

2006Another look at measures of forecast accuracy 2008Automatic Time Series Forecasting: The forecast Package for R 1993Forecasting methods and applications 2000The M3-Competition: results, conclusions and implications 1982The accuracy of extrapolation (time series) methods: Results of a forecasting competition 2002A state space framework for automatic forecasting using exponential smoothing methods 2017Automatic time series forecasting: the forecast package for R 2018The M4 Competition: Results, findings, conclusion and way forward

Cited by

2018Statistical and Machine Learning forecasting methods: Concerns and ways forward 2019The M4 Competition: 100,000 time series and 61 forecasting methods 2022Forecasting: theory and practice 2022M5 accuracy competition: Results, findings, and conclusions 2022Time Series and Regression Methods for Univariate Environmental Forecasting: An Empirical Evaluation 2020

Related works

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

2019The M4 Competition: 100,000 time series and 61 forecasting methodsarticle · 15 shared references 2019Forecasting in social settings: The state of the artarticle · 11 shared references 2022Forecast Selection and Representativenessarticle · 11 shared references 2018Statistical and Machine Learning forecasting methods: Concerns and ways forwardarticle · 10 shared references 2022M5 accuracy competition: Results, findings, and conclusionsarticle · 10 shared references 2011Forecasting monthly and quarterly time series using STL decompositionarticle · 10 shared references

Details

Year2019

Typearticle

Published2019-03-09

VenueInternational Journal of Forecasting

ISSN-L0169-2070

Languageen

Citations67

Files0 public / 0

ShelfForecasting Techniques and Applications

Identifiers

doi10.1016/j.ijforecast.2018.12.007

openalex_workW2921275503

Links

Imported from OpenAlex (CC0) — nothing here is generated.

Generalizing the Theta method for automatic forecasting

2019Machine learning in M4: What makes a good unstructured model?

2021FFORMPP: Feature-based forecast model performance prediction