Intelligent Data Analysis 24 (2020) 339–362 339 DOI 10.3233/IDA-184361 IOS Press Mining sequences in activities for time use analysis Jorge Rosales-Salas a , Sebastián Maldonado b,d,* and Alex Seret c a Centro de Economía y Políticas Sociales, Universidad Mayor, Chile b Department of Management Control and Information Systems, University of Chile, Santiago, Chile c Generation Research, Lasarettsgatan 13, 89133 Örnsköldsvik, Sweden d Instituto Sistemas Complejos de Ingeniería (ISCI), Chile Abstract. By providing a complete record of time use for a given population, time use studies enable investigators to test various hypotheses concerning that behavior. However, the large number and variety of activity combinations that are relevant in time allocation choices and, therefore, time use analysis, makes measuring or even fully identifying all of them impossible without the proper data mining tools. In this paper, we propose a framework for mining sequences of activities to capture more complex patterns than those currently available on how individuals organize their days. The proposed framework was applied to the American Time Use Surveys (ATUS) dataset to explore individual time allocation behavior, identifying sequences of activities that are frequent. For example, patterns such as the preferred activities that are performed before and after specific activities (such as paid work or leisure) are discussed in terms of their frequency. Such patterns are not easy to reveal using traditional descriptive analysis. Keywords: Time use, sequence mining analysis, data mining 1. Introduction The amount of time that individuals allocate to activities can be regarded as one of the most effective ways to ascertain the importance that people assign to their time, resulting from superimposing individ- ual preferences on institutionalized frameworks and collectively imposed conditions. Total time assigned to activities is constrained by the total time available, which causes an implicit valuation of the time al- located to various activities. The resulting time use is closely related to levels of social satisfaction and overall well-being [57]. However, time allocation research would ultimately be misled if it neglected the fact that societal be- havior is the result of choices made by millions of heterogeneous individuals with unique motivations, varying levels of information, and intrinsic purposes with which they carry out specific activities in a cho- sen order. The omission of patterns, sequences, and episode analysis results in inconsistent estimators, due to missing information. Sequence recognition and classification through data mining tools would allow the correct use of data to drive time use research in the desired direction. By obtaining information about the sequence of activities in which the members of society allocate their time, their individual * Corresponding author: Sebastián Maldonado, Facultad de Ingeniería y Ciencias Aplicadas, Universidad de los Andes, Mon- señor Álvaro del Portillo 12455, Las Condes, Santiago, Chile. Tel./Fax: +56 226181874, E-mail: smaldonado@uandes.cl. 1088-467X/20/$35.00 c  2020 – IOS Press and the authors. All rights reserved