Forecasting non-seasonal time series with missing observations


  • Journal: Journal of Forecasting, vol. 8, p. 97–116–20, Sunday 1. January 1989
  • Internasjonale standardnumre:
    • Trykt: 0277-6693
    • Elektronisk: 1099-131X
  • Lenke:

Most forecasting methods are based on equally spaced data. In the case of missing observations the methods have to be modified. We have considered three smoothing methods: namely, simple exponential smoothing; double exponential smoothing; and Holt's method. We present a new, unified approach to handle missing data within the smoothing methods. This approach is compared with previously suggested modifications. The comparison is done on 12 real, non-seasonal time series, and shows that the smoothing methods, properly modified, usually perform well if the time series have a moderate number of missing observations.