OverviewĪll forecast algorithms are simple models of a real-world data generating process (DGP).įor details on forecasting using an integer dimension, see Forecasting When No Date is in the View. ![]() For a high quality forecast, a simple pattern in the DGP must match the pattern described by the model reasonably well. Quality metrics measure how well the model matches the DGP. ![]() If the quality is low, the precision measured by the confidence bands is not important because it measures the precision of an inaccurate estimate. Tableau automatically selects the best of up to eight models, the best being the one that generates the highest quality forecast. The smoothing parameters of each model are optimized before Tableau assesses forecast quality. Therefore, choosing locally optimal smoothing parameters that are not also globally optimal is not impossible. However, initial value parameters are selected according to best practices but are not further optimized. So it is possible for initial value parameters to be less than optimal. The eight models available in Tableau are among those described at the following location on the OTexts web site: A taxonomy of exponential smoothing methods. When there is not enough data in the visualization, Tableau automatically tries to forecast at a finer temporal granularity, and then aggregates the forecast back to the granularity of the visualization. Tableau provides prediction bands which may be simulated or calculated from a closed form equation. All models with a multiplicative component or with aggregated forecasts have simulated bands, while all other models use the closed form equations. Exponential Smoothing and TrendĮxponential smoothing models iteratively forecast future values of a regular time series of values from weighted averages of past values of the series. The simplest model, Simple Exponential Smoothing, computes the next level or smoothed value from a weighted average of the last actual value and the last level value. The method is exponential because the value of each level is influenced by every preceding actual value to an exponentially decreasing degree-more recent values are given greater weight.Įxponential smoothing models with trend or seasonal components are effective when the measure to be forecast exhibits trend or seasonality over the period of time on which the forecast is based. ![]() Trend is a tendency in the data to increase or decrease over time. Seasonality is a repeating, predictable variation in value, such as an annual fluctuation in temperature relative to the season. In general, the more data points you have in your time series, the better the resulting forecast will be. Having enough data is particularly important if you want to model seasonality, because the model is more complicated and requires more proof in the form of data to achieve a reasonable level of precision. On the other hand, if you forecast using data generated by two or more different DGPs, you will get a lower quality forecast because a model can only match one. Tableau tests for a seasonal cycle with the length most typical for the time aggregation of the time series for which the forecast is estimated.
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