Asymmetric Time Aggregation and its Potential Benefits for Forecasting Annual Data
Robert Kunst () and
Philip Hans Franses
No 252, Economics Series from Institute for Advanced Studies
For many economic time-series variables that are observed regularly and frequently, for example weekly, the underlying activity is not distributed uniformly across the year. For the aim of predicting annual data, one may consider temporal aggregation into larger subannual units based on an activity time scale instead of calendar time. Such a scheme may strike a balance between annual modelling (which processes little information) and modelling at the finest available frequency (which may lead to an excessive parameter dimension), and it may also outperform modelling calendar time units (with some months or quarters containing more information than others). We suggest an algorithm that performs an approximate inversion of the inherent seasonal time deformation. We illustrate the procedure using weekly data for temporary staffing services.
Keywords: Seasonality; time deformation; prediction; time series (search for similar items in EconPapers)
JEL-codes: C22 C53 (search for similar items in EconPapers)
Pages: 30 pages
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https://irihs.ihs.ac.at/id/eprint/2000 First version, 2010 (application/pdf)
Journal Article: Asymmetric time aggregation and its potential benefits for forecasting annual data (2015)
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