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Evaluation of the Known Sub-Sequence Algorithm (KSSA) for Optimal Use in Time Series of Tuna Fishing in the Pacific Ocean
Evaluación del algoritmo de subsecuencias conocidas (KSSA) para su uso óptimo en series de tiempo de pesca de atún en el Océano Pacífico
DOI:
https://doi.org/10.15446/ing.investig.115627Keywords:
missing data, machine learning, imputation, data (en)aprendizaje automático, imputación, datos faltantes, datos (es)
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An important limitation in fisheries research using time series is the presence of missing data. An inadequate handling of these data can negatively impact the results of statistical analyses, leading to erroneous decision-making. A potential solution to this problem is the estimation of missing values through imputation methods, but none of them can be universally applied to all time series. In fact, their effectiveness largely depends on the structure of the data and the distribution of the missing values. Recently, a solution to this problem was proposed which employs the known sub-sequence algorithm (KSSA), a machine learning tehnique designed to compare the performance of different imputation methods and validate them within the time series that contains missing data. However, due to its recent development, there is no published evidence regarding its efficiency and reliability. This research aimed to assess the efficiency of the KSSA on tuna fisheries data for the Pacific Ocean by imputing simulated missing data for seven time series with distinct structures. The results demonstrated that the algorithm is robust for accurately validating a wide combination of properties, such as length, seasonality, trend, autocorrelation structure, and percentage of missing data. Additionally, the algorithm’s hyperparameters can be easily adjusted to achieve optimal results for each time series.
Una limitación importante en la investigación pesquera que utiliza series temporales es la presencia de datos faltantes. Un manejo inadecuado de estos datos puede afectar negativamente los resultados de los análisis estadísticos, conllevando una toma de decisiones errónea. Una posible solución a este problema es la estimación de valores faltantes mediante métodos de imputación, pero ninguno de ellos puede aplicarse universalmente a todas las series temporales. De hecho, su eficacia depende en gran medida de la estructura de los datos y de la distribución de los valores faltantes. Recientemente, se propuso una solución a este problema que emplea el algoritmo de sub-secuencias conocidas (KSSA), una técnica de aprendizaje automático diseñada para comparar el rendimiento de diferentes métodos de imputación y validarlos dentro de la serie temporal que contiene datos faltantes. Sin embargo, debido a su desarrollo reciente, no hay evidencia publicada sobre su eficiencia y fiabilidad. Esta investigación tuvo por objetivo evaluar la eficiencia del KSSA en datos de pesca de atún para el Océano Pacífico mediante la imputación de datos faltantes simulados para siete series temporales con estructuras distintas. Los resultados demostraron que el algoritmo es robusto para validar con precisión una amplia combinación de propiedades como la longitud, la estacionalidad, la tendencia, la estructura de autocorrelación y el porcentaje de datos faltantes. Además, los hiperparámetros del algoritmo pueden ajustarse fácilmente para lograr resultados óptimos en cada serie temporal.
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Copyright (c) 2025 Julian Gomez, Ivan Benavides, John Selvaraj
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