If the holdback=10, the LLB algorithm uses the last 10 data points of Y to make predictions for the last 10 data points of X.Ĭombines LLT and LLP models for its prediction.įor more information, see the predict command in the Search Reference.įorecasting seasonality with the x11 command For example, assume it uses dataset Y to make predictions for dataset X. LLB uses one set of data to make predictions for another. This is a bivariate model with no trends and no seasonality. This is a univariate model with trend but no seasonality. Requires the minimum number of data points to be twice the period. The periodicity of the time series is automatically computed. This is a univariate model with seasonality. This is a univariate model with no trends and no seasonality. Each of these algorithms are variations of the Kalman filter. You can select from the following algorithms with the predict command: LL, LLP, LLT, LLB, and LLP5. The x11 command, which is named after the X11 algorithm, removes seasonal fluctuations in fields to expose the real trend in your underlying data series.The predict command enables you to use different forecasting algorithms to predict future values of single and multivalue fields.The Splunk search language includes two forecasting commands: predict and x11. With this knowledge, you can proactively send alerts based on thresholds and perform "what-if" analyses to compare various scenarios. If there are patterns and correlations in events that you monitor, you can use them to predict future activity. You can use reports and dashboards to monitor activity as it is happening, then drill down into events and do a root-cause analysis to learn why something happened. Enhanced monitoring of key components which can detect system failures and prevent outages before they occur.Forecast earnings and other business metrics.Capacity planning to determine hardware requirements for virtual environments and forecast energy consumption.You can forecast time series data in a number of ways.
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