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Quality Quandaries : Forecasting with Seasonal Time Series Models

Summary: Quality Engineers today find themselves increasingly involved in a variety of company-wide operational problems, including improving the quality of forecasts as an aid to better planning, scheduling, and better customer service. In previous columns it was shown how autoregressive integrated moving average (ARIMA) models can be used to model stationary and non-stationary time series. The discussion is continued here with a demonstration of how seasonal data can be modeled and used for forecasting. International airline data originally modeled by Brown in 1962 are used to demonstrate how seasonal ARIMA models can model cyclic data useful for short term forecasting.

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  • Topics: Engineering
  • Keywords: Forecasting, ARMA (autoregressive moving average) model, ARIMA time series models, Problem solving
  • Author: Bisgaard, Soren; Kulahci, Murat
  • Journal: Quality Engineering