Forecasting Compositional Time Series with Exponential Smoothing Methods

@inproceedings{Koehler2010ForecastingCT,
  title={Forecasting Compositional Time Series with Exponential Smoothing Methods},
  author={Anne B. Koehler and Ralph D. Snyder and J. Keith Ord and Adrian Beaumont},
  year={2010}
}
Compositional time series are formed from measurements of proportions that sum to one in each period of time. We might be interested in forecasting the proportion of home loans that have adjustable rates, the proportion of nonagricultural jobs in manufacturing, the proportion of a specific oxide in the geochemical composition of a rock, or the proportion of an election betting market choosing a particular candidate. A problem may involve many related time series of proportions. There could be… CONTINUE READING

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