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This Concept Map, created with IHMC CmapTools, has information related to: Time series models, ARIMA is a method for estimation of A stationary processes, Autoregressive terms help to define a primary model for Estimation, The autocorrelation function is a basic tool for Identification, Identification involves finding Autoregressive terms, Smoothing methods e.g. Simple exponential smoothing, Smoothing methods e.g. Moving averages, Identification involves finding Moving average terms, Non-stationary processes include The random-walk model, Forecasts must be checked through an Evaluation process, Estimation is followed by Diagnostic checking procedures, A stationary processes can be obtained through Differencing, Time series models use Time series data, Differencing generates a stationay process for Estimation, Time series models e.g. Other, Simple exponential smoothing provide Forecasts, Time series models e.g ARIMA, Differencing generates a stationary process from Non-stationary processes, ARIMA uses The autocorrelation function, ARIMA uses The partial autocorrelation function, Forecasts can also evaluated based on Turning points