{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,18]],"date-time":"2026-07-18T04:51:22Z","timestamp":1784350282130,"version":"3.55.0"},"reference-count":44,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2020,1,10]],"date-time":"2020-01-10T00:00:00Z","timestamp":1578614400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Entropy is a key concept in the characterization of uncertainty for any given signal, and its extensions such as Spectral Entropy and Permutation Entropy. They have been used to measure the complexity of time series. However, these measures are subject to the discretization employed to study the states of the system, and identifying the relationship between complexity measures and the expected performance of the four selected forecasting methods that participate in the M4 Competition. This relationship allows the decision, in advance, of which algorithm is adequate. Therefore, in this paper, we found the relationships between entropy-based complexity framework and the forecasting error of four selected methods (Smyl, Theta, ARIMA, and ETS). Moreover, we present a framework extension based on the Emergence, Self-Organization, and Complexity paradigm. The experimentation with both synthetic and M4 Competition time series show that the feature space induced by complexities, visually constrains the forecasting method performance to specific regions; where the logarithm of its metric error is poorer, the Complexity based on the emergence and self-organization is maximal.<\/jats:p>","DOI":"10.3390\/e22010089","type":"journal-article","created":{"date-parts":[[2020,1,13]],"date-time":"2020-01-13T04:05:51Z","timestamp":1578888351000},"page":"89","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Time Series Complexities and Their Relationship to Forecasting Performance"],"prefix":"10.3390","volume":"22","author":[{"given":"Mirna","family":"Ponce-Flores","sequence":"first","affiliation":[{"name":"Graduate Program Division, Tecnol\u00f3gico Nacional de M\u00e9xico\/Instituto Tecnol\u00f3gico de Ciudad Madero, Cd. Madero 89440, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9307-0734","authenticated-orcid":false,"given":"Juan","family":"Frausto-Sol\u00eds","sequence":"additional","affiliation":[{"name":"Graduate Program Division, Tecnol\u00f3gico Nacional de M\u00e9xico\/Instituto Tecnol\u00f3gico de Ciudad Madero, Cd. Madero 89440, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4302-4902","authenticated-orcid":false,"given":"Guillermo","family":"Santamar\u00eda-Bonfil","sequence":"additional","affiliation":[{"name":"Information Technologies Department, Consejo Nacional de Ciencia y Tecnolog\u00eda\u2014Instituto Nacional de Electricidad y Energ\u00edas Limpias, Cuernavaca 62490, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joaqu\u00edn","family":"P\u00e9rez-Ortega","sequence":"additional","affiliation":[{"name":"Computing Department, Tecnol\u00f3gico Nacional de M\u00e9xico\/Centro Nacional de Investigaci\u00f3n y Desarrollo Tecnol\u00f3gico, Cuernavaca 62490, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juan J.","family":"Gonz\u00e1lez-Barbosa","sequence":"additional","affiliation":[{"name":"Graduate Program Division, Tecnol\u00f3gico Nacional de M\u00e9xico\/Instituto Tecnol\u00f3gico de Ciudad Madero, Cd. Madero 89440, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,1,10]]},"reference":[{"key":"ref_1","unstructured":"Montgomery, D.C., Jennings, C.L., and Kulahci, M. (2008). Introduction to Time Series Analysis and Forecasting, John Wiley & Sons."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.ijforecast.2019.02.012","article-title":"Predicting\/hypothesizing the findings of the M4 Competition","volume":"36","author":"Makridakis","year":"2019","journal-title":"Int. J. Forecast."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1007\/s10618-005-0039-x","article-title":"Characteristic-based clustering for time series data","volume":"13","author":"Wang","year":"2006","journal-title":"Data Min. Knowl. 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