An exponential moving average is a type of moving average, similar to a moving average, except that more weight is given to the latest data. It is also known as the exponentially weighted moving average


 Time Series – multiple (Single or multiple time series)


 Period [Mandatory]

 Relative_time  [default=10 days]


 Transformed Time Series - multiple

Available in

Alerts + Composite + Dashboards


The expMovingAverage function is useful in cases when you want to smooth a noisy metric.

Let’s take the following raw metric as an example, which counts the number of application errors in a given instance:


It is a sparse metric, as the majority of the input here is just zeros. In addition, the seasonality is not perfect, as the daily peaks emerge with some jitter. In fact, this noise hampers the baseline model selection, and leaves this metric inadequate for anomaly detection (all those daily surges will be falsely classified as anomalies).

After applying the expMovingAverage function (using a 45 minutes moving window), the baseline for this metric becomes much better:


Note that expMovingAverage smoothes the original time series, typically reducing the magnitude of the spikes. Here is the same spike, this time using a 60 minutes moving window: 


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