![]() ![]() Therefore, if there is a six-month cycle in your monthly time series, Tableau will probably find a 12-month pattern that contains two similar sub-patterns. So if you aggregate by months, Tableau will look for a 12-month cycle if you aggregate by quarters, Tableau will search for a four-quarter cycle and if you aggregate by days, Tableau will search for weekly seasonality. ![]() 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. 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. ![]() 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. In general, the more data points you have in your time series, the better the resulting forecast will be. Seasonality is a repeating, predictable variation in value, such as an annual fluctuation in temperature relative to the season. Trend is a tendency in the data to increase or decrease over time. 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. 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. 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. All models with a multiplicative component or with aggregated forecasts have simulated bands, while all other models use the closed form equations. Tableau provides prediction bands which may be simulated or calculated from a closed form equation. 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. 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. So it is possible for initial value parameters to be less than optimal. However, initial value parameters are selected according to best practices but are not further optimized. Therefore, choosing locally optimal smoothing parameters that are not also globally optimal is not impossible. The smoothing parameters of each model are optimized before Tableau assesses forecast quality. Tableau automatically selects the best of up to eight models, the best being the one that generates the highest quality forecast. If the quality is low, the precision measured by the confidence bands is not important because it measures the precision of an inaccurate estimate. Quality metrics measure how well the model matches the DGP. For a high quality forecast, a simple pattern in the DGP must match the pattern described by the model reasonably well. OverviewĪll forecast algorithms are simple models of a real-world data generating process (DGP). For details on forecasting using an integer dimension, see Forecasting When No Date is in the View. However, in the absence of a date, Tableau can create a forecast for a view that contains a dimension with integer values in addition to at least one measure.įor details on creating a forecast, see Create a Forecast. You typically add a forecast to a view that contains a date field and at least one measure. Use your (Link opens in a new window) account to sign in. Watch a video: To see related concepts demonstrated in Tableau, watch Forecasting (Link opens in a new window), a 6-minute free training video. If you’re interested in predictive modeling, also available in Tableau, see How Predictive Modeling Functions Work in Tableau. Forecast algorithms try to find a regular pattern in measures that can be continued into the future. Forecasting in Tableau uses a technique known as exponential smoothing. ![]()
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