Assessing methods for multiple imputation of systematic missing data in marine fisheries time series with a new validation algorithm

Aquaculture and Fisheries - Tập 8 - Trang 587-599 - 2023
Iván F. Benavides1, Marlon Santacruz2, Jhoana P. Romero-Leiton3,4, Carlos Barreto5, John Josephraj Selvaraj1
1Instituto de Estudios del Pacífico, Universidad Nacional de Colombia, Tumaco Campus, Kilómetro 30-31, Cajapí Vía Nacional Tumaco – Pasto, Tumaco, Nariño, 522020, Colombia
2Ingeniería en Producción Acuícola, Universidad de Nariño, Cra. 22 #30-63, Torre 4 apto. 703, San Juan de Pasto, Nariño, 520004, Colombia
3Facultad de ingeniería, Universidad Cesmag, Pasto, 520004, Colombia
4Datambiente, 520001, Colombia
5Autoridad Nacional de Pesca y Acuicultura (AUNAP), 520001, Colombia

Tài liệu tham khảo

Afrifa-Yamoah, 2020, Missing data imputation of high-resolution temporal climate time series data, Meteorological Applications, 27, 1, 10.1002/met.1873 Altamar, 2020, Reconstructed data of landings for the artisanal beach seine fishery in the marine-coastal area of Taganga, Colombian Caribbean Sea, Data in Brief, 30, 10.1016/j.dib.2020.105604 Beck, 2018 Bokde, 2018, A novel imputation methodology for time series based on pattern sequence forecasting, Pattern Recognition Letters, 116, 88, 10.1016/j.patrec.2018.09.020 Chandrasekaran, 2016, 1 Chan Coro, 2016, Analysing and forecasting fisheries time series: Purse seine in Indian ocean as a case study, ICES Journal of Marine Science: Journal Du Conseil, 73, 2552, 10.1093/icesjms/fsw131 De Valpine, 2002, Review of methods for fitting time-series models with process and observation error and likelihood calculations for nonlinear, non-Gaussian state-space models, Bulletin of Marine Science, 70, 455 Demirhan, 2018, Missing value imputation for short to mid-term horizontal solar irradiance data, Applied Energy, 225, 998, 10.1016/j.apenergy.2018.05.054 Donders, 2006, Review: A gentle introduction to imputation of missing values, Journal of Clinical Epidemiology, 59, 1087, 10.1016/j.jclinepi.2006.01.014 Duarte, 2019, 95 Engels, 2003, Imputation of missing longitudinal data: A comparison of methods, Journal of Clinical Epidemiology, 56, 968, 10.1016/S0895-4356(03)00170-7 2017 2020, The state of world fisheries and aquaculture, Sustainability in Action, 225 Farmer, 2015, Forecasting for recreational fisheries management: what's the catch?, North American Journal of Fisheries Management, 35, 720, 10.1080/02755947.2015.1044628 Genolini, 2013, Copy mean: A new method to impute intermittent missing values in longitudinal studies, Open Journal of Statistics, 3, 10.4236/ojs.2013.34A004 Golyandina, 2014, Basic singular Spectrum analysis and forecasting with R, Computational Statistics & Data Analysis, 71, 934, 10.1016/j.csda.2013.04.009 Hassani, 2019, Evaluating the performance of multiple imputation methods for handling missing values in time series data: A study focused on East Africa, soil-carbonate-stable isotope data, Stats, 2, 457, 10.3390/stats2040032 Huque, 2018, A comparison of multiple imputation methods for missing data in longitudinal studies, BMC Medical Research Methodology, 18 Hyndman, 2008, Automatic time series forecasting: The forecast package for R, Journal of Statistical Software, 27, 22, 10.18637/jss.v027.i03 Jamshidian, 2014, MissMech: An R package for testing homoscedasticity, multivariate normality, and missing completely at random (MCAR), Journal of Statistical Software, 56, 1, 10.18637/jss.v056.i06 Junger, 2015, Imputation of missing data in time series for air pollutants, Atmospheric Environment, 102, 96, 10.1016/j.atmosenv.2014.11.049 Kihoro, 2013, Imputation of incomplete nonstationary seasonal time series data, Mathematical Theory and Modeling, 3, 142 Koslow, 2016, Vol. 17 Little, 1987 Liu, 2020, Missing value imputation for industrial IoT sensor data with large gaps, IEEE Internet of Things Journal, 7, 6855, 10.1109/JIOT.2020.2970467 Litzow, 2009, Fishing through (and up) Alaskan food webs, Canadian Journal of Fisheries and Aquatic Sciences, 26, 10.1139/F08-207 Liu, 2019, Parameter estimation of heavy-tailed AR model with missing data via stochastic em, IEEE Transactions on Signal Processing, 67, 2159, 10.1109/TSP.2019.2899816 Lloret, 2000, Time series modelling of landings in Northwest Mediterranean Sea, ICES Journal of Marine Science, 57, 10.1006/jmsc.2000.0570 Magare, 2020, Imputation of missing data in time series by different computation methods in various data set applications. ITM Web of Conference, ITM Web of Conferences, 32, 10.1051/itmconf/20203203010 Mahmoudvand, 2016, Missing value imputation in time series using Singular Spectrum Analysis, International Journal of Energy and Statistics, 1650005, 10.1142/S2335680416500058 Moritz, 2017, imputeTS: Time series missing value imputation in R, R Journal, 9, 207, 10.32614/RJ-2017-009 Moritz Peacock, 2020, Evaluating the consequences of common assumptions in run reconstructions on pacific salmon biological status assessments, Canadian Journal of Fisheries and Aquatic Sciences, 77, 1904, 10.1139/cjfas-2019-0432 Phan, 2020, Dynamic time warping-based imputation for univariate time series data, Pattern Recognition Letters, 139, 10.1016/j.patrec.2017.08.019 Pikitch, 2004, Ecosystem-based fishery management, Science (Washington), 305, 346, 10.1126/science.1098222 Preciado, 2006, Using time series methods for completing fisheries historical series, Boletin del Instituto Espanol de Oceanografia, 22, 83 2021 Rubin, 1987 Rudd, 2016, Does unreported catch lead to overfishing?, Fish and Fisheries, 18, 10.1111/faf.12181 Schafer, 1997 Schafer, 1997 Schafer, 2002, Missing data: Our view of the state of the art, Psychological Methods, 7, 147, 10.1037/1082-989X.7.2.147 Selvaraj, 2020, Time-series modeling of fishery landings in the Colombian Pacific Ocean using an ARIMA model, Regional Studies in Marine Science, 39, 101477, 10.1016/j.rsma.2020.101477 2018 Shumway, 2011, p218 Spratt, 2010, Strategies for multiple imputation in longitudinal studies, American Journal of Epidemiology, 172, 478, 10.1093/aje/kwq137 Stergiou, 1996, Modelling and forecasting annual fisheries catches: Comparison of regression, univariate and multi-variate time series methods, Fisheries Research, 25, 105, 10.1016/0165-7836(95)00389-4 Wei, 2018, Missing value imputation approach for mass spectrometry-based metabolomics data, Nature Scientific Reports, 8 Wu, 2015, Time series forecasting with missing values, 151 Yozgatligil, 2013, Comparison of missing value imputation methods in time series: The case of Turkish meteorological data, Theoretical and Applied Climatology, 112, 143, 10.1007/s00704-012-0723-x Zar, 2009