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This Concept Map, created with IHMC CmapTools, has information related to: Time series models, Time series models e.g. Other, The autocorrelation function is a basic tool for Identification, Estimation generates at the end Forecasts, Simple exponential smoothing provide Forecasts, Identification involves finding the degree of Differencing, Smoothing methods e.g. Winter's exponential smoothing, A stationary processes can be obtained through Differencing, Moving average terms help to define a primary model for Estimation, Smoothing methods e.g. Moving averages, Identification involves finding Autoregressive terms, Smoothing methods e.g. Simple exponential smoothing, ARIMA uses The partial autocorrelation function, Forecasts can generate Combined forecasts, ARIMA uses The autocorrelation function, The partial autocorrelation function is a basic tool for Identification, Time series models use Time series data, Differencing generates a stationay process for Estimation, Moving averages provide Forecasts, Autoregressive terms help to define a primary model for Estimation, Differencing generates a stationary process from Non-stationary processes