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Este mapa conceptual tiene información relacionada a: Time series models, Forecasts can also evaluated based on Turning points, Moving averages provide Forecasts, Time series models use Time series data, Identification involves finding Autoregressive terms, Non-stationary processes include The random-walk model, ARIMA uses The autocorrelation function, Smoothing methods e.g. Simple exponential smoothing, Estimation generates at the end Forecasts, Time series models e.g ARIMA, Simple exponential smoothing provide Forecasts, Differencing generates a stationary process from Non-stationary processes, Moving average terms help to define a primary model for Estimation, Identification involves finding the degree of Differencing, Time series models e.g. Other, A stationary processes can be obtained through Differencing, Autoregressive terms help to define a primary model for Estimation, Smoothing methods e.g. Moving averages, Time series data can be characterized by their Components, Forecasts can generate Combined forecasts, The autocorrelation function is a basic tool for Identification