Forecasting using dynamic factor models with cluster structure at Barcelona subway stations
Fecha
2022
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Editor
Taylor & Francis Online
Resumen
Dynamic factor models are a powerful technique for analysing vast
volumes of data, more precisely, time series. However, the large
volumes of data that come from public transport networks tend
to have heterogeneity and a cluster structure. In this paper,
Dynamic Factor Models with Cluster Structure (DFMCS) are used
to forecast hourly entrances in the different stations of the
Barcelona subway network. The main and most novel
contribution lies in the use of clustering techniques to make an
initial grouping of the behaviour of the elements belonging to
the time series, in order to subsequently be able to predict future
patterns.
Descripción
Dynamic Factor Models (DFMs) are a valuable tool for analyzing extensive sets of time series data, particularly in the context of public transport networks. However, when dealing with the substantial and diverse data generated by these networks, characterized by heterogeneity and a cluster structure, challenges arise. This study introduces Dynamic Factor Models with Cluster Structure (DFMCS) and applies them to forecast hourly entries at various stations within the Barcelona subway network. A noteworthy innovation is the incorporation of clustering techniques, which enable an initial categorization of the time series elements' behavior. This clustering facilitates the prediction of future patterns by providing a structured understanding of the data
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Citación
Mariñas-Collado, I., Sipols, A. E., Santos-Martín, M. T., & Frutos-Bernal, E. (2022). Forecasting using dynamic factor models with cluster structure at Barcelona subway stations. Transportation Planning and Technology, 45(8), 671-685.
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