Abstract
Time series data has a crucial role in business. It reveals temporal trends and patterns, making it possible for decision-makers to make informed decisions and mitigate problems even before they happen. The existence of missing values in time series can bring difficulties in the analysis and lead to inaccurate conclusions. Thus, there is the need to solve this issue by performing missing data imputation on time series.
In this work, we propose a Focalize KNN that takes advantage of time series properties to perform missing data imputation. The approach is tested with different methods, combinations of parameters and features. The results of the proposed approach, with overlap and disjoint missing patterns, show Focalize KNN is very beneficial in scenarios with disjoint missing patterns.
This work is supported by FEDER, through POR LISBOA 2020 and COMPETE 2020 of the Portugal 2020 Project CityCatalyst POCI-01-0247-FEDER-046119. Ana Almeida acknowledges the Doctoral Grant from Fundação para a Ciência e Tecnologia (2021.06222.BD). Susana Brás is funded by national funds, European Regional Development Fund, FSE, through COMPETE2020 and FCT, in the scope of the framework contract foreseen in the numbers 4, 5 and 6 of the article 23, of the Decree-Law 57/2016, of August 29, changed by Law 57/2017, of July 19. We thank OpenWeather for providing the datasets.
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Almeida, A., Brás, S., Sargento, S., Pinto, F.C. (2023). Time Series Imputation in Faulty Systems. In: Pertusa, A., Gallego, A.J., Sánchez, J.A., Domingues, I. (eds) Pattern Recognition and Image Analysis. IbPRIA 2023. Lecture Notes in Computer Science, vol 14062. Springer, Cham. https://doi.org/10.1007/978-3-031-36616-1_3
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