Asymmetric time aggregation and its potential benefits for forecasting annual data

Robert Kunst, Philip Hans Franses

Publications: Contribution to journalArticlePeer Reviewed


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 timescale instead of calendar time. Such a scheme may strike a balance between annual modeling (which processes little information) and modeling at the finest available frequency (which may lead to an excessive parameter dimension), and it may also outperform modeling 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 two exemplary weekly time series.

Original languageEnglish
Pages (from-to)363 - 387
Number of pages25
JournalEmpirical Economics: a quarterly journal of the Institute for Advanced Studies, Vienna
Issue number1
Publication statusPublished - Aug 2015

Austrian Fields of Science 2012

  • 502025 Econometrics


  • Forecasting
  • Seasonality
  • Time deformation
  • Time series

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