Data Imputation in Related Time Series Using Fuzzy Set-Based Techniques

One of the main challenges faced by people who use data from empirical research in their work is missing data. In many scientific disciplines and industries there are references to time series. The suitability of several methods to imputation of the missing data in the study of mutual links between the analysed time series have been presented and tested in this work. In this paper, known methods of supplementing data in time series were enriched by the use of fuzzy sets and their processing was tested on unique data from experimental research and a transport company database. Fuzzy linguistic descriptors-based methods of missing data imputation in databases containing time series are discussed. The proposed method has a high efficiency, which have been proven in a series of experiments with both artificial and real datasets. The proposed methodologies have been tested on theoretical example and empirical data sets from various fields: (1) ecological data on changes in bird arrival dates in the context of climate change and (2) data describing the transport of containers between ports on the Mediterranean. Moreover, an important novelty of this work is, in particular, an application of fuzzy techniques to the correction of the datasets containing bird migration descriptions.

[1]  L. V. Sokolov,et al.  Effect of global warming on the timing of migration and breeding of passerine birds in the 20th century , 2006, Entomological Review.

[2]  Hasan Raja Naqvi,et al.  Data on time series analysis of land surface temperature variation in response to vegetation indices in twelve Wereda of Ethiopia using mono window, split window algorithm and spectral radiance model , 2019, Data in brief.

[3]  Robert Nowicki,et al.  Rough Neuro-Fuzzy Structures for Classification With Missing Data , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[4]  Stephen R. Baillie,et al.  Long-term changes in the migration phenology of UK breeding birds detected by large-scale citizen science recording schemes , 2016 .

[5]  William T. Scherer,et al.  Exploring Imputation Techniques for Missing Data in Transportation Management Systems , 2003 .

[6]  T. Sparks,et al.  Is earlier spring migration of Tatarstan warblers expected under climate warming? , 2007, International journal of biometeorology.

[7]  Guoqiang Shen,et al.  Origin–destination missing data estimation for freight transportation planning: a gravity model-based regression approach , 2014 .

[8]  Yinhai Wang,et al.  A hybrid approach to integrate fuzzy C-means based imputation method with genetic algorithm for missing traffic volume data estimation , 2015 .

[9]  Witold Pedrycz,et al.  Fuzzy C-Means clustering of incomplete data based on probabilistic information granules of missing values , 2016, Knowl. Based Syst..

[10]  Markus Ahola,et al.  Variation in climate warming along the migration route uncouples arrival and breeding dates , 2004 .

[11]  Ramon C. Littell,et al.  Statistical Methods in Agriculture and Experimental Biology. , 1985 .

[12]  Kathleen Hall Jamieson,et al.  The Effects of Zika Virus Risk Coverage on Familiarity, Knowledge and Behavior in the U.S. – A Time Series Analysis Combining Content Analysis and a Nationally Representative Survey , 2018, Health communication.

[13]  Jitender S. Deogun,et al.  Towards Missing Data Imputation: A Study of Fuzzy K-means Clustering Method , 2004, Rough Sets and Current Trends in Computing.

[14]  Roberto Ambrosini,et al.  Climate change effects on migration phenology may mismatch brood parasitic cuckoos and their hosts , 2009, Biology Letters.

[15]  Roger Mead,et al.  Statistical Methods in Agriculture and Experimental Biology. , 1984 .

[16]  Mehran Amiri,et al.  Missing data imputation using fuzzy-rough methods , 2016, Neurocomputing.

[17]  Mecislovas Zalakevicius,et al.  Spring arrival response to climate change in birds: a case study from eastern Europe , 2006, Journal of Ornithology.

[18]  Ignacy Kitowski,et al.  Trends in the Arrival Dates of Spring Migrants in Lublin (E Poland) , 2009 .

[19]  Niclas Jonzén,et al.  Change in spring arrival of migratory birds under an era of climate change, Swedish data from the last 140 years , 2015, AMBIO.

[20]  Igor Škrjanc,et al.  Incremental Missing-Data Imputation for Evolving Fuzzy Granular Prediction , 2020, IEEE Transactions on Fuzzy Systems.

[21]  Pengjian Shang,et al.  Financial time series analysis using the relation between MPE and MWPE , 2020 .

[22]  Dan Wang,et al.  Granular data imputation: A framework of Granular Computing , 2016, Appl. Soft Comput..

[23]  Yuichi Miyabara,et al.  Time-series analysis of phosphorus-depleted microbial communities in carbon/nitrogen-amended soils , 2020 .

[24]  Alessandro G. Di Nuovo,et al.  Missing data analysis with fuzzy C-Means: A study of its application in a psychological scenario , 2011, Expert Syst. Appl..

[25]  Stuart H M Butchart,et al.  Temporal shifts and temperature sensitivity of avian spring migratory phenology: a phylogenetic meta‐analysis , 2016, The Journal of animal ecology.

[26]  Adam Kiersztyn,et al.  The city changes the daily activity of urban adapters: Camera-traps study of Apodemus agrarius behaviour and new approaches to data analysis , 2020 .